Overview

Dataset statistics

Number of variables133
Number of observations194592
Missing cells188318
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory197.5 MiB
Average record size in memory1.0 KiB

Variable types

Numeric16
Categorical112
Text5

Alerts

Unnamed: 132 has constant value ""Constant
cont1 is highly overall correlated with cont6 and 5 other fieldsHigh correlation
cont3 is highly overall correlated with cat71High correlation
cont4 is highly overall correlated with cont8High correlation
cont6 is highly overall correlated with cont1 and 9 other fieldsHigh correlation
cont7 is highly overall correlated with cont6 and 4 other fieldsHigh correlation
cont8 is highly overall correlated with cont4High correlation
cont9 is highly overall correlated with cont1 and 9 other fieldsHigh correlation
cont10 is highly overall correlated with cont1 and 7 other fieldsHigh correlation
cont11 is highly overall correlated with cont1 and 5 other fieldsHigh correlation
cont12 is highly overall correlated with cont1 and 5 other fieldsHigh correlation
cont13 is highly overall correlated with cont6 and 7 other fieldsHigh correlation
cat1 is highly overall correlated with cat100High correlation
cat2 is highly overall correlated with cat9 and 1 other fieldsHigh correlation
cat3 is highly overall correlated with cat16 and 2 other fieldsHigh correlation
cat4 is highly overall correlated with cat23 and 2 other fieldsHigh correlation
cat5 is highly overall correlated with cat36 and 2 other fieldsHigh correlation
cat6 is highly overall correlated with cat50 and 2 other fieldsHigh correlation
cat7 is highly overall correlated with cat57 and 1 other fieldsHigh correlation
cat8 is highly overall correlated with cat66 and 1 other fieldsHigh correlation
cat9 is highly overall correlated with cat2 and 1 other fieldsHigh correlation
cat10 is highly overall correlated with cat12 and 1 other fieldsHigh correlation
cat11 is highly overall correlated with cat101High correlation
cat12 is highly overall correlated with cat10 and 1 other fieldsHigh correlation
cat13 is highly overall correlated with cat101High correlation
cat14 is highly overall correlated with cat76High correlation
cat16 is highly overall correlated with cat3 and 2 other fieldsHigh correlation
cat17 is highly overall correlated with cat90High correlation
cat23 is highly overall correlated with cat4 and 1 other fieldsHigh correlation
cat24 is highly overall correlated with cat28High correlation
cat25 is highly overall correlated with cat111High correlation
cat26 is highly overall correlated with cat111High correlation
cat27 is highly overall correlated with cat4 and 1 other fieldsHigh correlation
cat28 is highly overall correlated with cat24High correlation
cat36 is highly overall correlated with cat5 and 1 other fieldsHigh correlation
cat37 is highly overall correlated with cat5 and 1 other fieldsHigh correlation
cat38 is highly overall correlated with cat103High correlation
cat40 is highly overall correlated with cat41High correlation
cat41 is highly overall correlated with cat40High correlation
cat44 is highly overall correlated with cat103High correlation
cat49 is highly overall correlated with cat114High correlation
cat50 is highly overall correlated with cat6 and 2 other fieldsHigh correlation
cat52 is highly overall correlated with cat114High correlation
cat53 is highly overall correlated with cat114High correlation
cat57 is highly overall correlated with cat7 and 1 other fieldsHigh correlation
cat65 is highly overall correlated with cat102High correlation
cat66 is highly overall correlated with cat8 and 1 other fieldsHigh correlation
cat71 is highly overall correlated with cont3 and 1 other fieldsHigh correlation
cat73 is highly overall correlated with cat74 and 3 other fieldsHigh correlation
cat74 is highly overall correlated with cat73 and 3 other fieldsHigh correlation
cat76 is highly overall correlated with cat14 and 6 other fieldsHigh correlation
cat77 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat78 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat80 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat81 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat85 is highly overall correlated with cat76 and 5 other fieldsHigh correlation
cat86 is highly overall correlated with cont9 and 7 other fieldsHigh correlation
cat87 is highly overall correlated with cat3 and 7 other fieldsHigh correlation
cat88 is highly overall correlated with cat98 and 1 other fieldsHigh correlation
cat89 is highly overall correlated with cat7 and 1 other fieldsHigh correlation
cat90 is highly overall correlated with cat3 and 2 other fieldsHigh correlation
cat91 is highly overall correlated with cat73 and 3 other fieldsHigh correlation
cat92 is highly overall correlated with cat73 and 3 other fieldsHigh correlation
cat95 is highly overall correlated with cont13 and 4 other fieldsHigh correlation
cat96 is highly overall correlated with cat71High correlation
cat97 is highly overall correlated with cont1 and 7 other fieldsHigh correlation
cat98 is highly overall correlated with cont9 and 8 other fieldsHigh correlation
cat99 is highly overall correlated with cat86 and 3 other fieldsHigh correlation
cat100 is highly overall correlated with cat1 and 6 other fieldsHigh correlation
cat101 is highly overall correlated with cat2 and 5 other fieldsHigh correlation
cat102 is highly overall correlated with cat8 and 2 other fieldsHigh correlation
cat103 is highly overall correlated with cat5 and 4 other fieldsHigh correlation
cat104 is highly overall correlated with cont6 and 1 other fieldsHigh correlation
cat106 is highly overall correlated with cont6 and 1 other fieldsHigh correlation
cat107 is highly overall correlated with cat86 and 3 other fieldsHigh correlation
cat108 is highly overall correlated with cont13 and 6 other fieldsHigh correlation
cat111 is highly overall correlated with cat4 and 4 other fieldsHigh correlation
cat114 is highly overall correlated with cat6 and 4 other fieldsHigh correlation
cat115 is highly overall correlated with cont13 and 3 other fieldsHigh correlation
cat3 is highly imbalanced (69.4%)Imbalance
cat7 is highly imbalanced (83.6%)Imbalance
cat8 is highly imbalanced (67.8%)Imbalance
cat11 is highly imbalanced (51.0%)Imbalance
cat13 is highly imbalanced (52.1%)Imbalance
cat14 is highly imbalanced (90.6%)Imbalance
cat15 is highly imbalanced (99.8%)Imbalance
cat16 is highly imbalanced (78.4%)Imbalance
cat17 is highly imbalanced (94.0%)Imbalance
cat18 is highly imbalanced (95.3%)Imbalance
cat19 is highly imbalanced (92.2%)Imbalance
cat20 is highly imbalanced (98.8%)Imbalance
cat21 is highly imbalanced (97.7%)Imbalance
cat22 is highly imbalanced (99.7%)Imbalance
cat24 is highly imbalanced (78.8%)Imbalance
cat25 is highly imbalanced (53.9%)Imbalance
cat26 is highly imbalanced (67.4%)Imbalance
cat27 is highly imbalanced (51.0%)Imbalance
cat28 is highly imbalanced (76.2%)Imbalance
cat29 is highly imbalanced (85.9%)Imbalance
cat30 is highly imbalanced (86.4%)Imbalance
cat31 is highly imbalanced (81.4%)Imbalance
cat32 is highly imbalanced (94.4%)Imbalance
cat33 is highly imbalanced (95.4%)Imbalance
cat34 is highly imbalanced (97.0%)Imbalance
cat35 is highly imbalanced (98.7%)Imbalance
cat38 is highly imbalanced (52.8%)Imbalance
cat39 is highly imbalanced (82.5%)Imbalance
cat40 is highly imbalanced (74.1%)Imbalance
cat41 is highly imbalanced (76.7%)Imbalance
cat42 is highly imbalanced (92.6%)Imbalance
cat43 is highly imbalanced (84.5%)Imbalance
cat44 is highly imbalanced (58.8%)Imbalance
cat45 is highly imbalanced (84.2%)Imbalance
cat46 is highly imbalanced (95.7%)Imbalance
cat47 is highly imbalanced (96.5%)Imbalance
cat48 is highly imbalanced (98.4%)Imbalance
cat49 is highly imbalanced (71.9%)Imbalance
cat51 is highly imbalanced (94.3%)Imbalance
cat52 is highly imbalanced (72.8%)Imbalance
cat53 is highly imbalanced (59.2%)Imbalance
cat54 is highly imbalanced (83.6%)Imbalance
cat55 is highly imbalanced (99.1%)Imbalance
cat56 is highly imbalanced (98.9%)Imbalance
cat57 is highly imbalanced (88.1%)Imbalance
cat58 is highly imbalanced (98.6%)Imbalance
cat59 is highly imbalanced (98.3%)Imbalance
cat60 is highly imbalanced (97.6%)Imbalance
cat61 is highly imbalanced (96.4%)Imbalance
cat62 is highly imbalanced (99.7%)Imbalance
cat63 is highly imbalanced (99.5%)Imbalance
cat64 is highly imbalanced (99.7%)Imbalance
cat65 is highly imbalanced (90.6%)Imbalance
cat66 is highly imbalanced (73.8%)Imbalance
cat67 is highly imbalanced (96.5%)Imbalance
cat68 is highly imbalanced (99.1%)Imbalance
cat69 is highly imbalanced (98.2%)Imbalance
cat70 is highly imbalanced (99.8%)Imbalance
cat71 is highly imbalanced (70.8%)Imbalance
cat73 is highly imbalanced (56.9%)Imbalance
cat74 is highly imbalanced (91.3%)Imbalance
cat75 is highly imbalanced (57.0%)Imbalance
cat76 is highly imbalanced (84.4%)Imbalance
cat77 is highly imbalanced (97.7%)Imbalance
cat78 is highly imbalanced (95.4%)Imbalance
cat79 is highly imbalanced (55.9%)Imbalance
cat80 is highly imbalanced (51.5%)Imbalance
cat81 is highly imbalanced (57.1%)Imbalance
cat84 is highly imbalanced (61.2%)Imbalance
cat85 is highly imbalanced (94.3%)Imbalance
cat87 is highly imbalanced (67.8%)Imbalance
cat88 is highly imbalanced (75.9%)Imbalance
cat89 is highly imbalanced (94.2%)Imbalance
cat90 is highly imbalanced (88.2%)Imbalance
cat92 is highly imbalanced (65.9%)Imbalance
cat93 is highly imbalanced (65.1%)Imbalance
cat94 is highly imbalanced (55.3%)Imbalance
cat96 is highly imbalanced (83.4%)Imbalance
cat102 is highly imbalanced (87.2%)Imbalance
cat103 is highly imbalanced (57.6%)Imbalance
cat105 is highly imbalanced (51.6%)Imbalance
cat111 is highly imbalanced (64.5%)Imbalance
cat114 is highly imbalanced (61.5%)Imbalance
Unnamed: 132 has 188318 (96.8%) missing valuesMissing

Reproduction

Analysis started2023-09-25 03:54:06.723320
Analysis finished2023-09-25 03:59:52.769106
Duration5 minutes and 46.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct188318
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303280.48
Minimum1
Maximum587633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T21:59:53.135240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30908.55
Q1152556.75
median304310.5
Q3455284.25
95-th percentile572321.45
Maximum587633
Range587632
Interquartile range (IQR)302727.5

Descriptive statistics

Standard deviation173957.48
Coefficient of variation (CV)0.57358614
Kurtosis-1.2176313
Mean303280.48
Median Absolute Deviation (MAD)151373
Skewness-0.019864156
Sum5.9015956 × 1010
Variance3.0261206 × 1010
MonotonicityNot monotonic
2023-09-24T21:59:53.396508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
587633 2
 
< 0.1%
574489 2
 
< 0.1%
574499 2
 
< 0.1%
574500 2
 
< 0.1%
574502 2
 
< 0.1%
574503 2
 
< 0.1%
574506 2
 
< 0.1%
574508 2
 
< 0.1%
574509 2
 
< 0.1%
574517 2
 
< 0.1%
Other values (188308) 194572
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
5 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
24 1
< 0.1%
ValueCountFrequency (%)
587633 2
< 0.1%
587632 2
< 0.1%
587630 2
< 0.1%
587624 2
< 0.1%
587620 2
< 0.1%
587619 2
< 0.1%
587612 2
< 0.1%
587611 2
< 0.1%
587607 2
< 0.1%
587606 2
< 0.1%

cat1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
146214 
B
48378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 146214
75.1%
B 48378
 
24.9%

Length

2023-09-24T21:59:53.612253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:53.792137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 146214
75.1%
b 48378
 
24.9%

Most occurring characters

ValueCountFrequency (%)
A 146214
75.1%
B 48378
 
24.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 146214
75.1%
B 48378
 
24.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 146214
75.1%
B 48378
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 146214
75.1%
B 48378
 
24.9%

cat2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
110253 
B
84339 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
A 110253
56.7%
B 84339
43.3%

Length

2023-09-24T21:59:53.984146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:54.159998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 110253
56.7%
b 84339
43.3%

Most occurring characters

ValueCountFrequency (%)
A 110253
56.7%
B 84339
43.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 110253
56.7%
B 84339
43.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 110253
56.7%
B 84339
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 110253
56.7%
B 84339
43.3%

cat3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
183937 
B
 
10655

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 183937
94.5%
B 10655
 
5.5%

Length

2023-09-24T21:59:54.356066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:54.528542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 183937
94.5%
b 10655
 
5.5%

Most occurring characters

ValueCountFrequency (%)
A 183937
94.5%
B 10655
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 183937
94.5%
B 10655
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 183937
94.5%
B 10655
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 183937
94.5%
B 10655
 
5.5%

cat4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
132672 
B
61920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
A 132672
68.2%
B 61920
31.8%

Length

2023-09-24T21:59:54.720852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:54.905319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 132672
68.2%
b 61920
31.8%

Most occurring characters

ValueCountFrequency (%)
A 132672
68.2%
B 61920
31.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 132672
68.2%
B 61920
31.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 132672
68.2%
B 61920
31.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 132672
68.2%
B 61920
31.8%

cat5
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
127844 
B
66748 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 127844
65.7%
B 66748
34.3%

Length

2023-09-24T21:59:55.361904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:55.536966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 127844
65.7%
b 66748
34.3%

Most occurring characters

ValueCountFrequency (%)
A 127844
65.7%
B 66748
34.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 127844
65.7%
B 66748
34.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 127844
65.7%
B 66748
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 127844
65.7%
B 66748
34.3%

cat6
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
136134 
B
58458 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 136134
70.0%
B 58458
30.0%

Length

2023-09-24T21:59:55.715117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:55.893986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 136134
70.0%
b 58458
30.0%

Most occurring characters

ValueCountFrequency (%)
A 136134
70.0%
B 58458
30.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 136134
70.0%
B 58458
30.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 136134
70.0%
B 58458
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 136134
70.0%
B 58458
30.0%

cat7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
189885 
B
 
4707

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 189885
97.6%
B 4707
 
2.4%

Length

2023-09-24T21:59:56.067525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:56.231901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 189885
97.6%
b 4707
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A 189885
97.6%
B 4707
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 189885
97.6%
B 4707
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 189885
97.6%
B 4707
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 189885
97.6%
B 4707
 
2.4%

cat8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
183169 
B
 
11423

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 183169
94.1%
B 11423
 
5.9%

Length

2023-09-24T21:59:56.402512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:56.575213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 183169
94.1%
b 11423
 
5.9%

Most occurring characters

ValueCountFrequency (%)
A 183169
94.1%
B 11423
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 183169
94.1%
B 11423
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 183169
94.1%
B 11423
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 183169
94.1%
B 11423
 
5.9%

cat9
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
116880 
B
77712 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
A 116880
60.1%
B 77712
39.9%

Length

2023-09-24T21:59:56.748643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:56.914659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 116880
60.1%
b 77712
39.9%

Most occurring characters

ValueCountFrequency (%)
A 116880
60.1%
B 77712
39.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 116880
60.1%
B 77712
39.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 116880
60.1%
B 77712
39.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 116880
60.1%
B 77712
39.9%

cat10
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
165576 
B
29016 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 165576
85.1%
B 29016
 
14.9%

Length

2023-09-24T21:59:57.098171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:57.268980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 165576
85.1%
b 29016
 
14.9%

Most occurring characters

ValueCountFrequency (%)
A 165576
85.1%
B 29016
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 165576
85.1%
B 29016
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 165576
85.1%
B 29016
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 165576
85.1%
B 29016
 
14.9%

cat11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
173800 
B
20792 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 173800
89.3%
B 20792
 
10.7%

Length

2023-09-24T21:59:57.463481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:57.633681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 173800
89.3%
b 20792
 
10.7%

Most occurring characters

ValueCountFrequency (%)
A 173800
89.3%
B 20792
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 173800
89.3%
B 20792
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 173800
89.3%
B 20792
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 173800
89.3%
B 20792
 
10.7%

cat12
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
165140 
B
29452 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 165140
84.9%
B 29452
 
15.1%

Length

2023-09-24T21:59:57.806788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:57.975749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 165140
84.9%
b 29452
 
15.1%

Most occurring characters

ValueCountFrequency (%)
A 165140
84.9%
B 29452
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 165140
84.9%
B 29452
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 165140
84.9%
B 29452
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 165140
84.9%
B 29452
 
15.1%

cat13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
174489 
B
20103 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 174489
89.7%
B 20103
 
10.3%

Length

2023-09-24T21:59:58.154483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:58.341363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 174489
89.7%
b 20103
 
10.3%

Most occurring characters

ValueCountFrequency (%)
A 174489
89.7%
B 20103
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 174489
89.7%
B 20103
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 174489
89.7%
B 20103
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 174489
89.7%
B 20103
 
10.3%

cat14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
192244 
B
 
2348

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 192244
98.8%
B 2348
 
1.2%

Length

2023-09-24T21:59:58.526763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:58.692894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 192244
98.8%
b 2348
 
1.2%

Most occurring characters

ValueCountFrequency (%)
A 192244
98.8%
B 2348
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 192244
98.8%
B 2348
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 192244
98.8%
B 2348
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 192244
98.8%
B 2348
 
1.2%

cat15
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194557 
B
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194557
> 99.9%
B 35
 
< 0.1%

Length

2023-09-24T21:59:58.878232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:59.055088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194557
> 99.9%
b 35
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 194557
> 99.9%
B 35
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194557
> 99.9%
B 35
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194557
> 99.9%
B 35
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194557
> 99.9%
B 35
 
< 0.1%

cat16
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
187909 
B
 
6683

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 187909
96.6%
B 6683
 
3.4%

Length

2023-09-24T21:59:59.243145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:59.404944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 187909
96.6%
b 6683
 
3.4%

Most occurring characters

ValueCountFrequency (%)
A 187909
96.6%
B 6683
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 187909
96.6%
B 6683
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 187909
96.6%
B 6683
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 187909
96.6%
B 6683
 
3.4%

cat17
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193248 
B
 
1344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193248
99.3%
B 1344
 
0.7%

Length

2023-09-24T21:59:59.590199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T21:59:59.755871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193248
99.3%
b 1344
 
0.7%

Most occurring characters

ValueCountFrequency (%)
A 193248
99.3%
B 1344
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193248
99.3%
B 1344
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193248
99.3%
B 1344
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193248
99.3%
B 1344
 
0.7%

cat18
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193577 
B
 
1015

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193577
99.5%
B 1015
 
0.5%

Length

2023-09-24T21:59:59.950922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:00.124148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193577
99.5%
b 1015
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 193577
99.5%
B 1015
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193577
99.5%
B 1015
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193577
99.5%
B 1015
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193577
99.5%
B 1015
 
0.5%

cat19
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
192724 
B
 
1868

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 192724
99.0%
B 1868
 
1.0%

Length

2023-09-24T22:00:00.299275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:00.470526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 192724
99.0%
b 1868
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A 192724
99.0%
B 1868
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 192724
99.0%
B 1868
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 192724
99.0%
B 1868
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 192724
99.0%
B 1868
 
1.0%

cat20
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194378 
B
 
214

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194378
99.9%
B 214
 
0.1%

Length

2023-09-24T22:00:00.648962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:00.823837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194378
99.9%
b 214
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 194378
99.9%
B 214
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194378
99.9%
B 214
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194378
99.9%
B 214
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194378
99.9%
B 214
 
0.1%

cat21
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194163 
B
 
429

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194163
99.8%
B 429
 
0.2%

Length

2023-09-24T22:00:01.017183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:01.196991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194163
99.8%
b 429
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 194163
99.8%
B 429
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194163
99.8%
B 429
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194163
99.8%
B 429
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194163
99.8%
B 429
 
0.2%

cat22
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194549 
B
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194549
> 99.9%
B 43
 
< 0.1%

Length

2023-09-24T22:00:01.382716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:01.545310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194549
> 99.9%
b 43
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 194549
> 99.9%
B 43
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194549
> 99.9%
B 43
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194549
> 99.9%
B 43
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194549
> 99.9%
B 43
 
< 0.1%

cat23
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
162728 
B
31864 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
A 162728
83.6%
B 31864
 
16.4%

Length

2023-09-24T22:00:01.719727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:01.886001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 162728
83.6%
b 31864
 
16.4%

Most occurring characters

ValueCountFrequency (%)
A 162728
83.6%
B 31864
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 162728
83.6%
B 31864
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 162728
83.6%
B 31864
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 162728
83.6%
B 31864
 
16.4%

cat24
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
188043 
B
 
6549

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 188043
96.6%
B 6549
 
3.4%

Length

2023-09-24T22:00:02.072907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:02.242366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 188043
96.6%
b 6549
 
3.4%

Most occurring characters

ValueCountFrequency (%)
A 188043
96.6%
B 6549
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 188043
96.6%
B 6549
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 188043
96.6%
B 6549
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 188043
96.6%
B 6549
 
3.4%

cat25
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
175633 
B
18959 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 175633
90.3%
B 18959
 
9.7%

Length

2023-09-24T22:00:02.431313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:02.598851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 175633
90.3%
b 18959
 
9.7%

Most occurring characters

ValueCountFrequency (%)
A 175633
90.3%
B 18959
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 175633
90.3%
B 18959
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 175633
90.3%
B 18959
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 175633
90.3%
B 18959
 
9.7%

cat26
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
182971 
B
 
11621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 182971
94.0%
B 11621
 
6.0%

Length

2023-09-24T22:00:02.801896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:02.976045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 182971
94.0%
b 11621
 
6.0%

Most occurring characters

ValueCountFrequency (%)
A 182971
94.0%
B 11621
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 182971
94.0%
B 11621
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 182971
94.0%
B 11621
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 182971
94.0%
B 11621
 
6.0%

cat27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
173813 
B
20779 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 173813
89.3%
B 20779
 
10.7%

Length

2023-09-24T22:00:03.160621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:03.322719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 173813
89.3%
b 20779
 
10.7%

Most occurring characters

ValueCountFrequency (%)
A 173813
89.3%
B 20779
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 173813
89.3%
B 20779
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 173813
89.3%
B 20779
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 173813
89.3%
B 20779
 
10.7%

cat28
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
186978 
B
 
7614

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 186978
96.1%
B 7614
 
3.9%

Length

2023-09-24T22:00:03.503010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:03.668807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 186978
96.1%
b 7614
 
3.9%

Most occurring characters

ValueCountFrequency (%)
A 186978
96.1%
B 7614
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 186978
96.1%
B 7614
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 186978
96.1%
B 7614
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 186978
96.1%
B 7614
 
3.9%

cat29
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
190732 
B
 
3860

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 190732
98.0%
B 3860
 
2.0%

Length

2023-09-24T22:00:03.850481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:04.007941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 190732
98.0%
b 3860
 
2.0%

Most occurring characters

ValueCountFrequency (%)
A 190732
98.0%
B 3860
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 190732
98.0%
B 3860
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 190732
98.0%
B 3860
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 190732
98.0%
B 3860
 
2.0%

cat30
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
190888 
B
 
3704

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 190888
98.1%
B 3704
 
1.9%

Length

2023-09-24T22:00:04.193777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:04.361918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 190888
98.1%
b 3704
 
1.9%

Most occurring characters

ValueCountFrequency (%)
A 190888
98.1%
B 3704
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 190888
98.1%
B 3704
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 190888
98.1%
B 3704
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 190888
98.1%
B 3704
 
1.9%

cat31
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
189067 
B
 
5525

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 189067
97.2%
B 5525
 
2.8%

Length

2023-09-24T22:00:04.535298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:04.692933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 189067
97.2%
b 5525
 
2.8%

Most occurring characters

ValueCountFrequency (%)
A 189067
97.2%
B 5525
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 189067
97.2%
B 5525
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 189067
97.2%
B 5525
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 189067
97.2%
B 5525
 
2.8%

cat32
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193345 
B
 
1247

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193345
99.4%
B 1247
 
0.6%

Length

2023-09-24T22:00:05.145514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:05.328923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193345
99.4%
b 1247
 
0.6%

Most occurring characters

ValueCountFrequency (%)
A 193345
99.4%
B 1247
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193345
99.4%
B 1247
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193345
99.4%
B 1247
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193345
99.4%
B 1247
 
0.6%

cat33
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193600 
B
 
992

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193600
99.5%
B 992
 
0.5%

Length

2023-09-24T22:00:05.514353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:05.675780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193600
99.5%
b 992
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 193600
99.5%
B 992
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193600
99.5%
B 992
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193600
99.5%
B 992
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193600
99.5%
B 992
 
0.5%

cat34
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193987 
B
 
605

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193987
99.7%
B 605
 
0.3%

Length

2023-09-24T22:00:05.866115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:06.045153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193987
99.7%
b 605
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A 193987
99.7%
B 605
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193987
99.7%
B 605
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193987
99.7%
B 605
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193987
99.7%
B 605
 
0.3%

cat35
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194372 
B
 
220

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194372
99.9%
B 220
 
0.1%

Length

2023-09-24T22:00:06.247903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:06.412663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194372
99.9%
b 220
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 194372
99.9%
B 220
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194372
99.9%
B 220
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194372
99.9%
B 220
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194372
99.9%
B 220
 
0.1%

cat36
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
161522 
B
33070 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 161522
83.0%
B 33070
 
17.0%

Length

2023-09-24T22:00:06.588425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:06.782956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 161522
83.0%
b 33070
 
17.0%

Most occurring characters

ValueCountFrequency (%)
A 161522
83.0%
B 33070
 
17.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 161522
83.0%
B 33070
 
17.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 161522
83.0%
B 33070
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 161522
83.0%
B 33070
 
17.0%

cat37
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
171210 
B
23382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 171210
88.0%
B 23382
 
12.0%

Length

2023-09-24T22:00:06.999398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:07.210131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 171210
88.0%
b 23382
 
12.0%

Most occurring characters

ValueCountFrequency (%)
A 171210
88.0%
B 23382
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 171210
88.0%
B 23382
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 171210
88.0%
B 23382
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 171210
88.0%
B 23382
 
12.0%

cat38
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
174947 
B
19645 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 174947
89.9%
B 19645
 
10.1%

Length

2023-09-24T22:00:07.417261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:07.589340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 174947
89.9%
b 19645
 
10.1%

Most occurring characters

ValueCountFrequency (%)
A 174947
89.9%
B 19645
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 174947
89.9%
B 19645
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 174947
89.9%
B 19645
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 174947
89.9%
B 19645
 
10.1%

cat39
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
189487 
B
 
5105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 189487
97.4%
B 5105
 
2.6%

Length

2023-09-24T22:00:07.774941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:07.945672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 189487
97.4%
b 5105
 
2.6%

Most occurring characters

ValueCountFrequency (%)
A 189487
97.4%
B 5105
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 189487
97.4%
B 5105
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 189487
97.4%
B 5105
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 189487
97.4%
B 5105
 
2.6%

cat40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
186087 
B
 
8505

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 186087
95.6%
B 8505
 
4.4%

Length

2023-09-24T22:00:08.131949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:08.296298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 186087
95.6%
b 8505
 
4.4%

Most occurring characters

ValueCountFrequency (%)
A 186087
95.6%
B 8505
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 186087
95.6%
B 8505
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 186087
95.6%
B 8505
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 186087
95.6%
B 8505
 
4.4%

cat41
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
187190 
B
 
7402

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 187190
96.2%
B 7402
 
3.8%

Length

2023-09-24T22:00:08.493153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:08.678568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 187190
96.2%
b 7402
 
3.8%

Most occurring characters

ValueCountFrequency (%)
A 187190
96.2%
B 7402
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 187190
96.2%
B 7402
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 187190
96.2%
B 7402
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 187190
96.2%
B 7402
 
3.8%

cat42
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
192829 
B
 
1763

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 192829
99.1%
B 1763
 
0.9%

Length

2023-09-24T22:00:08.872225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:09.044450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 192829
99.1%
b 1763
 
0.9%

Most occurring characters

ValueCountFrequency (%)
A 192829
99.1%
B 1763
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 192829
99.1%
B 1763
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 192829
99.1%
B 1763
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 192829
99.1%
B 1763
 
0.9%

cat43
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
190238 
B
 
4354

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 190238
97.8%
B 4354
 
2.2%

Length

2023-09-24T22:00:09.234000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:09.424556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 190238
97.8%
b 4354
 
2.2%

Most occurring characters

ValueCountFrequency (%)
A 190238
97.8%
B 4354
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 190238
97.8%
B 4354
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 190238
97.8%
B 4354
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 190238
97.8%
B 4354
 
2.2%

cat44
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
178467 
B
 
16125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 178467
91.7%
B 16125
 
8.3%

Length

2023-09-24T22:00:09.626129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:09.822176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 178467
91.7%
b 16125
 
8.3%

Most occurring characters

ValueCountFrequency (%)
A 178467
91.7%
B 16125
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 178467
91.7%
B 16125
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 178467
91.7%
B 16125
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 178467
91.7%
B 16125
 
8.3%

cat45
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
190126 
B
 
4466

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 190126
97.7%
B 4466
 
2.3%

Length

2023-09-24T22:00:10.015794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:10.188441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 190126
97.7%
b 4466
 
2.3%

Most occurring characters

ValueCountFrequency (%)
A 190126
97.7%
B 4466
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 190126
97.7%
B 4466
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 190126
97.7%
B 4466
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 190126
97.7%
B 4466
 
2.3%

cat46
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193682 
B
 
910

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193682
99.5%
B 910
 
0.5%

Length

2023-09-24T22:00:10.373336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:10.532913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193682
99.5%
b 910
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 193682
99.5%
B 910
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193682
99.5%
B 910
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193682
99.5%
B 910
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193682
99.5%
B 910
 
0.5%

cat47
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193867 
B
 
725

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193867
99.6%
B 725
 
0.4%

Length

2023-09-24T22:00:10.709484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:10.868186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193867
99.6%
b 725
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 193867
99.6%
B 725
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193867
99.6%
B 725
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193867
99.6%
B 725
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193867
99.6%
B 725
 
0.4%

cat48
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194309 
B
 
283

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194309
99.9%
B 283
 
0.1%

Length

2023-09-24T22:00:11.060831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:11.238186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194309
99.9%
b 283
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 194309
99.9%
B 283
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194309
99.9%
B 283
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194309
99.9%
B 283
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194309
99.9%
B 283
 
0.1%

cat49
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
185121 
B
 
9471

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 185121
95.1%
B 9471
 
4.9%

Length

2023-09-24T22:00:11.421790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:11.592428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 185121
95.1%
b 9471
 
4.9%

Most occurring characters

ValueCountFrequency (%)
A 185121
95.1%
B 9471
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 185121
95.1%
B 9471
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 185121
95.1%
B 9471
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 185121
95.1%
B 9471
 
4.9%

cat50
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
142251 
B
52341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 142251
73.1%
B 52341
 
26.9%

Length

2023-09-24T22:00:11.770986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:11.943703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 142251
73.1%
b 52341
 
26.9%

Most occurring characters

ValueCountFrequency (%)
A 142251
73.1%
B 52341
 
26.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 142251
73.1%
B 52341
 
26.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 142251
73.1%
B 52341
 
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 142251
73.1%
B 52341
 
26.9%

cat51
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193304 
B
 
1288

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193304
99.3%
B 1288
 
0.7%

Length

2023-09-24T22:00:12.124984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:12.295177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193304
99.3%
b 1288
 
0.7%

Most occurring characters

ValueCountFrequency (%)
A 193304
99.3%
B 1288
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193304
99.3%
B 1288
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193304
99.3%
B 1288
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193304
99.3%
B 1288
 
0.7%

cat52
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
185494 
B
 
9098

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 185494
95.3%
B 9098
 
4.7%

Length

2023-09-24T22:00:12.470375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:12.627131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 185494
95.3%
b 9098
 
4.7%

Most occurring characters

ValueCountFrequency (%)
A 185494
95.3%
B 9098
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 185494
95.3%
B 9098
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 185494
95.3%
B 9098
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 185494
95.3%
B 9098
 
4.7%

cat53
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
178705 
B
 
15887

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 178705
91.8%
B 15887
 
8.2%

Length

2023-09-24T22:00:12.808822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:12.966704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 178705
91.8%
b 15887
 
8.2%

Most occurring characters

ValueCountFrequency (%)
A 178705
91.8%
B 15887
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 178705
91.8%
B 15887
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 178705
91.8%
B 15887
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 178705
91.8%
B 15887
 
8.2%

cat54
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
189883 
B
 
4709

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 189883
97.6%
B 4709
 
2.4%

Length

2023-09-24T22:00:13.148961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:13.314002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 189883
97.6%
b 4709
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A 189883
97.6%
B 4709
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 189883
97.6%
B 4709
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 189883
97.6%
B 4709
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 189883
97.6%
B 4709
 
2.4%

cat55
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194441 
B
 
151

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194441
99.9%
B 151
 
0.1%

Length

2023-09-24T22:00:13.513048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:13.674768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194441
99.9%
b 151
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 194441
99.9%
B 151
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194441
99.9%
B 151
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194441
99.9%
B 151
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194441
99.9%
B 151
 
0.1%

cat56
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194401 
B
 
191

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194401
99.9%
B 191
 
0.1%

Length

2023-09-24T22:00:13.848510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:14.014224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194401
99.9%
b 191
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 194401
99.9%
B 191
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194401
99.9%
B 191
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194401
99.9%
B 191
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194401
99.9%
B 191
 
0.1%

cat57
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
191473 
B
 
3119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 191473
98.4%
B 3119
 
1.6%

Length

2023-09-24T22:00:14.188211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:14.374772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 191473
98.4%
b 3119
 
1.6%

Most occurring characters

ValueCountFrequency (%)
A 191473
98.4%
B 3119
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 191473
98.4%
B 3119
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 191473
98.4%
B 3119
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 191473
98.4%
B 3119
 
1.6%

cat58
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194349 
B
 
243

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194349
99.9%
B 243
 
0.1%

Length

2023-09-24T22:00:14.578724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:14.789511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194349
99.9%
b 243
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 194349
99.9%
B 243
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194349
99.9%
B 243
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194349
99.9%
B 243
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194349
99.9%
B 243
 
0.1%

cat59
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194286 
B
 
306

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194286
99.8%
B 306
 
0.2%

Length

2023-09-24T22:00:15.246283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:15.437275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194286
99.8%
b 306
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 194286
99.8%
B 306
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194286
99.8%
B 306
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194286
99.8%
B 306
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194286
99.8%
B 306
 
0.2%

cat60
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194136 
B
 
456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194136
99.8%
B 456
 
0.2%

Length

2023-09-24T22:00:15.633878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:15.820598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194136
99.8%
b 456
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 194136
99.8%
B 456
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194136
99.8%
B 456
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194136
99.8%
B 456
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194136
99.8%
B 456
 
0.2%

cat61
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193850 
B
 
742

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193850
99.6%
B 742
 
0.4%

Length

2023-09-24T22:00:16.020997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:16.194337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193850
99.6%
b 742
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 193850
99.6%
B 742
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193850
99.6%
B 742
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193850
99.6%
B 742
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193850
99.6%
B 742
 
0.4%

cat62
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194547 
B
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194547
> 99.9%
B 45
 
< 0.1%

Length

2023-09-24T22:00:16.396870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:16.581626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194547
> 99.9%
b 45
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 194547
> 99.9%
B 45
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194547
> 99.9%
B 45
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194547
> 99.9%
B 45
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194547
> 99.9%
B 45
 
< 0.1%

cat63
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194511 
B
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194511
> 99.9%
B 81
 
< 0.1%

Length

2023-09-24T22:00:16.794344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:16.958144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194511
> 99.9%
b 81
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 194511
> 99.9%
B 81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194511
> 99.9%
B 81
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194511
> 99.9%
B 81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194511
> 99.9%
B 81
 
< 0.1%

cat64
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194545 
B
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194545
> 99.9%
B 47
 
< 0.1%

Length

2023-09-24T22:00:17.132625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:17.290499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194545
> 99.9%
b 47
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 194545
> 99.9%
B 47
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194545
> 99.9%
B 47
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194545
> 99.9%
B 47
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194545
> 99.9%
B 47
 
< 0.1%

cat65
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
192251 
B
 
2341

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 192251
98.8%
B 2341
 
1.2%

Length

2023-09-24T22:00:17.470233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:17.638026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 192251
98.8%
b 2341
 
1.2%

Most occurring characters

ValueCountFrequency (%)
A 192251
98.8%
B 2341
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 192251
98.8%
B 2341
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 192251
98.8%
B 2341
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 192251
98.8%
B 2341
 
1.2%

cat66
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
185965 
B
 
8627

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 185965
95.6%
B 8627
 
4.4%

Length

2023-09-24T22:00:17.819263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:17.984483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 185965
95.6%
b 8627
 
4.4%

Most occurring characters

ValueCountFrequency (%)
A 185965
95.6%
B 8627
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 185965
95.6%
B 8627
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 185965
95.6%
B 8627
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 185965
95.6%
B 8627
 
4.4%

cat67
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
193884 
B
 
708

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 193884
99.6%
B 708
 
0.4%

Length

2023-09-24T22:00:18.164457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:18.321440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 193884
99.6%
b 708
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 193884
99.6%
B 708
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 193884
99.6%
B 708
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 193884
99.6%
B 708
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 193884
99.6%
B 708
 
0.4%

cat68
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194450 
B
 
142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194450
99.9%
B 142
 
0.1%

Length

2023-09-24T22:00:18.500537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:18.667760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194450
99.9%
b 142
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 194450
99.9%
B 142
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194450
99.9%
B 142
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194450
99.9%
B 142
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194450
99.9%
B 142
 
0.1%

cat69
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194271 
B
 
321

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194271
99.8%
B 321
 
0.2%

Length

2023-09-24T22:00:18.852597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:19.034008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194271
99.8%
b 321
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 194271
99.8%
B 321
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194271
99.8%
B 321
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194271
99.8%
B 321
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194271
99.8%
B 321
 
0.2%

cat70
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
194566 
B
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 194566
> 99.9%
B 26
 
< 0.1%

Length

2023-09-24T22:00:19.212741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:19.372537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 194566
> 99.9%
b 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 194566
> 99.9%
B 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 194566
> 99.9%
B 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 194566
> 99.9%
B 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 194566
> 99.9%
B 26
 
< 0.1%

cat71
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
184616 
B
 
9976

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 184616
94.9%
B 9976
 
5.1%

Length

2023-09-24T22:00:19.542522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:19.698047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 184616
94.9%
b 9976
 
5.1%

Most occurring characters

ValueCountFrequency (%)
A 184616
94.9%
B 9976
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 184616
94.9%
B 9976
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 184616
94.9%
B 9976
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 184616
94.9%
B 9976
 
5.1%

cat72
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
122289 
B
72303 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 122289
62.8%
B 72303
37.2%

Length

2023-09-24T22:00:19.871606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:20.035900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 122289
62.8%
b 72303
37.2%

Most occurring characters

ValueCountFrequency (%)
A 122289
62.8%
B 72303
37.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 122289
62.8%
B 72303
37.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 122289
62.8%
B 72303
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 122289
62.8%
B 72303
37.2%

cat73
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
159373 
B
35193 
C
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 159373
81.9%
B 35193
 
18.1%
C 26
 
< 0.1%

Length

2023-09-24T22:00:20.215022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:20.380662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 159373
81.9%
b 35193
 
18.1%
c 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 159373
81.9%
B 35193
 
18.1%
C 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 159373
81.9%
B 35193
 
18.1%
C 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 159373
81.9%
B 35193
 
18.1%
C 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 159373
81.9%
B 35193
 
18.1%
C 26
 
< 0.1%

cat74
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
190887 
B
 
3679
C
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 190887
98.1%
B 3679
 
1.9%
C 26
 
< 0.1%

Length

2023-09-24T22:00:20.572978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:20.748134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 190887
98.1%
b 3679
 
1.9%
c 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 190887
98.1%
B 3679
 
1.9%
C 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 190887
98.1%
B 3679
 
1.9%
C 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 190887
98.1%
B 3679
 
1.9%
C 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 190887
98.1%
B 3679
 
1.9%
C 26
 
< 0.1%

cat75
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
159436 
B
35155 
C
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 159436
81.9%
B 35155
 
18.1%
C 1
 
< 0.1%

Length

2023-09-24T22:00:20.942555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:21.113713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 159436
81.9%
b 35155
 
18.1%
c 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 159436
81.9%
B 35155
 
18.1%
C 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 159436
81.9%
B 35155
 
18.1%
C 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 159436
81.9%
B 35155
 
18.1%
C 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 159436
81.9%
B 35155
 
18.1%
C 1
 
< 0.1%

cat76
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
187387 
B
 
6388
C
 
817

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 187387
96.3%
B 6388
 
3.3%
C 817
 
0.4%

Length

2023-09-24T22:00:21.304809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:21.468061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 187387
96.3%
b 6388
 
3.3%
c 817
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 187387
96.3%
B 6388
 
3.3%
C 817
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 187387
96.3%
B 6388
 
3.3%
C 817
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 187387
96.3%
B 6388
 
3.3%
C 817
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 187387
96.3%
B 6388
 
3.3%
C 817
 
0.4%

cat77
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
D
193747 
C
 
428
B
 
368
A
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 193747
99.6%
C 428
 
0.2%
B 368
 
0.2%
A 49
 
< 0.1%

Length

2023-09-24T22:00:21.642235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:21.820609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 193747
99.6%
c 428
 
0.2%
b 368
 
0.2%
a 49
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 193747
99.6%
C 428
 
0.2%
B 368
 
0.2%
A 49
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 193747
99.6%
C 428
 
0.2%
B 368
 
0.2%
A 49
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 193747
99.6%
C 428
 
0.2%
B 368
 
0.2%
A 49
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 193747
99.6%
C 428
 
0.2%
B 368
 
0.2%
A 49
 
< 0.1%

cat78
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
192736 
A
 
817
C
 
669
D
 
370

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 192736
99.0%
A 817
 
0.4%
C 669
 
0.3%
D 370
 
0.2%

Length

2023-09-24T22:00:22.011956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:22.182007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 192736
99.0%
a 817
 
0.4%
c 669
 
0.3%
d 370
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B 192736
99.0%
A 817
 
0.4%
C 669
 
0.3%
D 370
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 192736
99.0%
A 817
 
0.4%
C 669
 
0.3%
D 370
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 192736
99.0%
A 817
 
0.4%
C 669
 
0.3%
D 370
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 192736
99.0%
A 817
 
0.4%
C 669
 
0.3%
D 370
 
0.2%

cat79
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
158003 
D
27573 
A
 
7290
C
 
1726

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowD

Common Values

ValueCountFrequency (%)
B 158003
81.2%
D 27573
 
14.2%
A 7290
 
3.7%
C 1726
 
0.9%

Length

2023-09-24T22:00:22.371996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:22.565194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 158003
81.2%
d 27573
 
14.2%
a 7290
 
3.7%
c 1726
 
0.9%

Most occurring characters

ValueCountFrequency (%)
B 158003
81.2%
D 27573
 
14.2%
A 7290
 
3.7%
C 1726
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 158003
81.2%
D 27573
 
14.2%
A 7290
 
3.7%
C 1726
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 158003
81.2%
D 27573
 
14.2%
A 7290
 
3.7%
C 1726
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 158003
81.2%
D 27573
 
14.2%
A 7290
 
3.7%
C 1726
 
0.9%

cat80
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
D
142071 
B
48110 
C
 
3599
A
 
812

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowB
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
D 142071
73.0%
B 48110
 
24.7%
C 3599
 
1.8%
A 812
 
0.4%

Length

2023-09-24T22:00:22.760203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:22.937628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 142071
73.0%
b 48110
 
24.7%
c 3599
 
1.8%
a 812
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D 142071
73.0%
B 48110
 
24.7%
C 3599
 
1.8%
A 812
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 142071
73.0%
B 48110
 
24.7%
C 3599
 
1.8%
A 812
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 142071
73.0%
B 48110
 
24.7%
C 3599
 
1.8%
A 812
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 142071
73.0%
B 48110
 
24.7%
C 3599
 
1.8%
A 812
 
0.4%

cat81
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
D
159542 
B
24913 
C
 
9320
A
 
817

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 159542
82.0%
B 24913
 
12.8%
C 9320
 
4.8%
A 817
 
0.4%

Length

2023-09-24T22:00:23.117151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:23.295782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 159542
82.0%
b 24913
 
12.8%
c 9320
 
4.8%
a 817
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D 159542
82.0%
B 24913
 
12.8%
C 9320
 
4.8%
A 817
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 159542
82.0%
B 24913
 
12.8%
C 9320
 
4.8%
A 817
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 159542
82.0%
B 24913
 
12.8%
C 9320
 
4.8%
A 817
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 159542
82.0%
B 24913
 
12.8%
C 9320
 
4.8%
A 817
 
0.4%

cat82
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
152435 
A
19967 
D
19448 
C
 
2742

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowB
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
B 152435
78.3%
A 19967
 
10.3%
D 19448
 
10.0%
C 2742
 
1.4%

Length

2023-09-24T22:00:23.475398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:23.642665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 152435
78.3%
a 19967
 
10.3%
d 19448
 
10.0%
c 2742
 
1.4%

Most occurring characters

ValueCountFrequency (%)
B 152435
78.3%
A 19967
 
10.3%
D 19448
 
10.0%
C 2742
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 152435
78.3%
A 19967
 
10.3%
D 19448
 
10.0%
C 2742
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 152435
78.3%
A 19967
 
10.3%
D 19448
 
10.0%
C 2742
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 152435
78.3%
A 19967
 
10.3%
D 19448
 
10.0%
C 2742
 
1.4%

cat83
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
146212 
A
26918 
D
16346 
C
 
5116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowB
3rd rowD
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 146212
75.1%
A 26918
 
13.8%
D 16346
 
8.4%
C 5116
 
2.6%

Length

2023-09-24T22:00:23.835363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:24.006027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 146212
75.1%
a 26918
 
13.8%
d 16346
 
8.4%
c 5116
 
2.6%

Most occurring characters

ValueCountFrequency (%)
B 146212
75.1%
A 26918
 
13.8%
D 16346
 
8.4%
C 5116
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 146212
75.1%
A 26918
 
13.8%
D 16346
 
8.4%
C 5116
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 146212
75.1%
A 26918
 
13.8%
D 16346
 
8.4%
C 5116
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 146212
75.1%
A 26918
 
13.8%
D 16346
 
8.4%
C 5116
 
2.6%

cat84
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
C
160127 
A
30398 
D
 
3628
B
 
439

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 160127
82.3%
A 30398
 
15.6%
D 3628
 
1.9%
B 439
 
0.2%

Length

2023-09-24T22:00:24.193184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:24.360350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 160127
82.3%
a 30398
 
15.6%
d 3628
 
1.9%
b 439
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 160127
82.3%
A 30398
 
15.6%
D 3628
 
1.9%
B 439
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 160127
82.3%
A 30398
 
15.6%
D 3628
 
1.9%
B 439
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 160127
82.3%
A 30398
 
15.6%
D 3628
 
1.9%
B 439
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 160127
82.3%
A 30398
 
15.6%
D 3628
 
1.9%
B 439
 
0.2%

cat85
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
192197 
C
 
1044
A
 
817
D
 
534

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 192197
98.8%
C 1044
 
0.5%
A 817
 
0.4%
D 534
 
0.3%

Length

2023-09-24T22:00:24.543181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:24.714934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 192197
98.8%
c 1044
 
0.5%
a 817
 
0.4%
d 534
 
0.3%

Most occurring characters

ValueCountFrequency (%)
B 192197
98.8%
C 1044
 
0.5%
A 817
 
0.4%
D 534
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 192197
98.8%
C 1044
 
0.5%
A 817
 
0.4%
D 534
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 192197
98.8%
C 1044
 
0.5%
A 817
 
0.4%
D 534
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 192197
98.8%
C 1044
 
0.5%
A 817
 
0.4%
D 534
 
0.3%

cat86
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
107306 
D
74993 
C
 
10642
A
 
1651

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowB
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
B 107306
55.1%
D 74993
38.5%
C 10642
 
5.5%
A 1651
 
0.8%

Length

2023-09-24T22:00:24.908984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:25.074085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 107306
55.1%
d 74993
38.5%
c 10642
 
5.5%
a 1651
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 107306
55.1%
D 74993
38.5%
C 10642
 
5.5%
A 1651
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 107306
55.1%
D 74993
38.5%
C 10642
 
5.5%
A 1651
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 107306
55.1%
D 74993
38.5%
C 10642
 
5.5%
A 1651
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 107306
55.1%
D 74993
38.5%
C 10642
 
5.5%
A 1651
 
0.8%

cat87
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
172535 
D
 
12114
C
 
9126
A
 
817

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowC

Common Values

ValueCountFrequency (%)
B 172535
88.7%
D 12114
 
6.2%
C 9126
 
4.7%
A 817
 
0.4%

Length

2023-09-24T22:00:25.269870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:25.714329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 172535
88.7%
d 12114
 
6.2%
c 9126
 
4.7%
a 817
 
0.4%

Most occurring characters

ValueCountFrequency (%)
B 172535
88.7%
D 12114
 
6.2%
C 9126
 
4.7%
A 817
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 172535
88.7%
D 12114
 
6.2%
C 9126
 
4.7%
A 817
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 172535
88.7%
D 12114
 
6.2%
C 9126
 
4.7%
A 817
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 172535
88.7%
D 12114
 
6.2%
C 9126
 
4.7%
A 817
 
0.4%

cat88
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
174565 
D
19934 
E
 
86
B
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 174565
89.7%
D 19934
 
10.2%
E 86
 
< 0.1%
B 7
 
< 0.1%

Length

2023-09-24T22:00:25.898181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:26.074903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 174565
89.7%
d 19934
 
10.2%
e 86
 
< 0.1%
b 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 174565
89.7%
D 19934
 
10.2%
E 86
 
< 0.1%
B 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 174565
89.7%
D 19934
 
10.2%
E 86
 
< 0.1%
B 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 174565
89.7%
D 19934
 
10.2%
E 86
 
< 0.1%
B 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 174565
89.7%
D 19934
 
10.2%
E 86
 
< 0.1%
B 7
 
< 0.1%

cat89
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
189885 
B
 
4439
C
 
226
D
 
33
E
 
5
Other values (3)
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 189885
97.6%
B 4439
 
2.3%
C 226
 
0.1%
D 33
 
< 0.1%
E 5
 
< 0.1%
I 2
 
< 0.1%
H 1
 
< 0.1%
G 1
 
< 0.1%

Length

2023-09-24T22:00:26.271791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:26.467088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 189885
97.6%
b 4439
 
2.3%
c 226
 
0.1%
d 33
 
< 0.1%
e 5
 
< 0.1%
i 2
 
< 0.1%
h 1
 
< 0.1%
g 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 189885
97.6%
B 4439
 
2.3%
C 226
 
0.1%
D 33
 
< 0.1%
E 5
 
< 0.1%
I 2
 
< 0.1%
H 1
 
< 0.1%
G 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 189885
97.6%
B 4439
 
2.3%
C 226
 
0.1%
D 33
 
< 0.1%
E 5
 
< 0.1%
I 2
 
< 0.1%
H 1
 
< 0.1%
G 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 189885
97.6%
B 4439
 
2.3%
C 226
 
0.1%
D 33
 
< 0.1%
E 5
 
< 0.1%
I 2
 
< 0.1%
H 1
 
< 0.1%
G 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 189885
97.6%
B 4439
 
2.3%
C 226
 
0.1%
D 33
 
< 0.1%
E 5
 
< 0.1%
I 2
 
< 0.1%
H 1
 
< 0.1%
G 1
 
< 0.1%

cat90
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
183937 
B
 
9819
C
 
753
D
 
71
E
 
6
Other values (2)
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 183937
94.5%
B 9819
 
5.0%
C 753
 
0.4%
D 71
 
< 0.1%
E 6
 
< 0.1%
F 4
 
< 0.1%
G 2
 
< 0.1%

Length

2023-09-24T22:00:26.662142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:26.854608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 183937
94.5%
b 9819
 
5.0%
c 753
 
0.4%
d 71
 
< 0.1%
e 6
 
< 0.1%
f 4
 
< 0.1%
g 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 183937
94.5%
B 9819
 
5.0%
C 753
 
0.4%
D 71
 
< 0.1%
E 6
 
< 0.1%
F 4
 
< 0.1%
G 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 183937
94.5%
B 9819
 
5.0%
C 753
 
0.4%
D 71
 
< 0.1%
E 6
 
< 0.1%
F 4
 
< 0.1%
G 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 183937
94.5%
B 9819
 
5.0%
C 753
 
0.4%
D 71
 
< 0.1%
E 6
 
< 0.1%
F 4
 
< 0.1%
G 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 183937
94.5%
B 9819
 
5.0%
C 753
 
0.4%
D 71
 
< 0.1%
E 6
 
< 0.1%
F 4
 
< 0.1%
G 2
 
< 0.1%

cat91
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
114746 
B
44049 
G
27622 
C
 
6594
D
 
1195
Other values (3)
 
386

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
A 114746
59.0%
B 44049
 
22.6%
G 27622
 
14.2%
C 6594
 
3.4%
D 1195
 
0.6%
E 261
 
0.1%
F 99
 
0.1%
H 26
 
< 0.1%

Length

2023-09-24T22:00:27.046382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:27.233030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 114746
59.0%
b 44049
 
22.6%
g 27622
 
14.2%
c 6594
 
3.4%
d 1195
 
0.6%
e 261
 
0.1%
f 99
 
0.1%
h 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 114746
59.0%
B 44049
 
22.6%
G 27622
 
14.2%
C 6594
 
3.4%
D 1195
 
0.6%
E 261
 
0.1%
F 99
 
0.1%
H 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 114746
59.0%
B 44049
 
22.6%
G 27622
 
14.2%
C 6594
 
3.4%
D 1195
 
0.6%
E 261
 
0.1%
F 99
 
0.1%
H 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 114746
59.0%
B 44049
 
22.6%
G 27622
 
14.2%
C 6594
 
3.4%
D 1195
 
0.6%
E 261
 
0.1%
F 99
 
0.1%
H 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 114746
59.0%
B 44049
 
22.6%
G 27622
 
14.2%
C 6594
 
3.4%
D 1195
 
0.6%
E 261
 
0.1%
F 99
 
0.1%
H 26
 
< 0.1%

cat92
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
128816 
H
65019 
B
 
656
C
 
63
I
 
26
Other values (2)
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowH

Common Values

ValueCountFrequency (%)
A 128816
66.2%
H 65019
33.4%
B 656
 
0.3%
C 63
 
< 0.1%
I 26
 
< 0.1%
D 11
 
< 0.1%
F 1
 
< 0.1%

Length

2023-09-24T22:00:27.433081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:27.611999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 128816
66.2%
h 65019
33.4%
b 656
 
0.3%
c 63
 
< 0.1%
i 26
 
< 0.1%
d 11
 
< 0.1%
f 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 128816
66.2%
H 65019
33.4%
B 656
 
0.3%
C 63
 
< 0.1%
I 26
 
< 0.1%
D 11
 
< 0.1%
F 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 128816
66.2%
H 65019
33.4%
B 656
 
0.3%
C 63
 
< 0.1%
I 26
 
< 0.1%
D 11
 
< 0.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 128816
66.2%
H 65019
33.4%
B 656
 
0.3%
C 63
 
< 0.1%
I 26
 
< 0.1%
D 11
 
< 0.1%
F 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 128816
66.2%
H 65019
33.4%
B 656
 
0.3%
C 63
 
< 0.1%
I 26
 
< 0.1%
D 11
 
< 0.1%
F 1
 
< 0.1%

cat93
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
D
155219 
C
37005 
B
 
1172
E
 
745
A
 
451

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 155219
79.8%
C 37005
 
19.0%
B 1172
 
0.6%
E 745
 
0.4%
A 451
 
0.2%

Length

2023-09-24T22:00:27.810215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:28.000497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 155219
79.8%
c 37005
 
19.0%
b 1172
 
0.6%
e 745
 
0.4%
a 451
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D 155219
79.8%
C 37005
 
19.0%
B 1172
 
0.6%
E 745
 
0.4%
A 451
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 155219
79.8%
C 37005
 
19.0%
B 1172
 
0.6%
E 745
 
0.4%
A 451
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 155219
79.8%
C 37005
 
19.0%
B 1172
 
0.6%
E 745
 
0.4%
A 451
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 155219
79.8%
C 37005
 
19.0%
B 1172
 
0.6%
E 745
 
0.4%
A 451
 
0.2%

cat94
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
D
125638 
B
53469 
C
14101 
A
 
757
F
 
511
Other values (2)
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowD
3rd rowD
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
D 125638
64.6%
B 53469
27.5%
C 14101
 
7.2%
A 757
 
0.4%
F 511
 
0.3%
E 96
 
< 0.1%
G 20
 
< 0.1%

Length

2023-09-24T22:00:28.178989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:28.374355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 125638
64.6%
b 53469
27.5%
c 14101
 
7.2%
a 757
 
0.4%
f 511
 
0.3%
e 96
 
< 0.1%
g 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 125638
64.6%
B 53469
27.5%
C 14101
 
7.2%
A 757
 
0.4%
F 511
 
0.3%
E 96
 
< 0.1%
G 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 125638
64.6%
B 53469
27.5%
C 14101
 
7.2%
A 757
 
0.4%
F 511
 
0.3%
E 96
 
< 0.1%
G 20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 125638
64.6%
B 53469
27.5%
C 14101
 
7.2%
A 757
 
0.4%
F 511
 
0.3%
E 96
 
< 0.1%
G 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 125638
64.6%
B 53469
27.5%
C 14101
 
7.2%
A 757
 
0.4%
F 511
 
0.3%
E 96
 
< 0.1%
G 20
 
< 0.1%

cat95
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
C
90445 
D
82174 
E
18004 
A
 
3857
B
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowD

Common Values

ValueCountFrequency (%)
C 90445
46.5%
D 82174
42.2%
E 18004
 
9.3%
A 3857
 
2.0%
B 112
 
0.1%

Length

2023-09-24T22:00:28.560814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:28.737238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 90445
46.5%
d 82174
42.2%
e 18004
 
9.3%
a 3857
 
2.0%
b 112
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 90445
46.5%
D 82174
42.2%
E 18004
 
9.3%
A 3857
 
2.0%
B 112
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 90445
46.5%
D 82174
42.2%
E 18004
 
9.3%
A 3857
 
2.0%
B 112
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 90445
46.5%
D 82174
42.2%
E 18004
 
9.3%
A 3857
 
2.0%
B 112
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 90445
46.5%
D 82174
42.2%
E 18004
 
9.3%
A 3857
 
2.0%
B 112
 
0.1%

cat96
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
E
180205 
D
 
8167
B
 
3047
G
 
2750
F
 
351
Other values (3)
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 180205
92.6%
D 8167
 
4.2%
B 3047
 
1.6%
G 2750
 
1.4%
F 351
 
0.2%
A 36
 
< 0.1%
C 24
 
< 0.1%
I 12
 
< 0.1%

Length

2023-09-24T22:00:28.929979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:29.114246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 180205
92.6%
d 8167
 
4.2%
b 3047
 
1.6%
g 2750
 
1.4%
f 351
 
0.2%
a 36
 
< 0.1%
c 24
 
< 0.1%
i 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 180205
92.6%
D 8167
 
4.2%
B 3047
 
1.6%
G 2750
 
1.4%
F 351
 
0.2%
A 36
 
< 0.1%
C 24
 
< 0.1%
I 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 180205
92.6%
D 8167
 
4.2%
B 3047
 
1.6%
G 2750
 
1.4%
F 351
 
0.2%
A 36
 
< 0.1%
C 24
 
< 0.1%
I 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 180205
92.6%
D 8167
 
4.2%
B 3047
 
1.6%
G 2750
 
1.4%
F 351
 
0.2%
A 36
 
< 0.1%
C 24
 
< 0.1%
I 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 180205
92.6%
D 8167
 
4.2%
B 3047
 
1.6%
G 2750
 
1.4%
F 351
 
0.2%
A 36
 
< 0.1%
C 24
 
< 0.1%
I 12
 
< 0.1%

cat97
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
C
80709 
E
49064 
A
43331 
G
17318 
D
 
3916
Other values (2)
 
254

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
C 80709
41.5%
E 49064
25.2%
A 43331
22.3%
G 17318
 
8.9%
D 3916
 
2.0%
F 219
 
0.1%
B 35
 
< 0.1%

Length

2023-09-24T22:00:29.337405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:29.533173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 80709
41.5%
e 49064
25.2%
a 43331
22.3%
g 17318
 
8.9%
d 3916
 
2.0%
f 219
 
0.1%
b 35
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 80709
41.5%
E 49064
25.2%
A 43331
22.3%
G 17318
 
8.9%
D 3916
 
2.0%
F 219
 
0.1%
B 35
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 80709
41.5%
E 49064
25.2%
A 43331
22.3%
G 17318
 
8.9%
D 3916
 
2.0%
F 219
 
0.1%
B 35
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 80709
41.5%
E 49064
25.2%
A 43331
22.3%
G 17318
 
8.9%
D 3916
 
2.0%
F 219
 
0.1%
B 35
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 80709
41.5%
E 49064
25.2%
A 43331
22.3%
G 17318
 
8.9%
D 3916
 
2.0%
F 219
 
0.1%
B 35
 
< 0.1%

cat98
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
109011 
D
52250 
C
22183 
E
 
10591
B
 
557

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowD
3rd rowA
4th rowD
5th rowA

Common Values

ValueCountFrequency (%)
A 109011
56.0%
D 52250
26.9%
C 22183
 
11.4%
E 10591
 
5.4%
B 557
 
0.3%

Length

2023-09-24T22:00:29.724644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:29.914499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 109011
56.0%
d 52250
26.9%
c 22183
 
11.4%
e 10591
 
5.4%
b 557
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A 109011
56.0%
D 52250
26.9%
C 22183
 
11.4%
E 10591
 
5.4%
B 557
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 109011
56.0%
D 52250
26.9%
C 22183
 
11.4%
E 10591
 
5.4%
B 557
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 109011
56.0%
D 52250
26.9%
C 22183
 
11.4%
E 10591
 
5.4%
B 557
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 109011
56.0%
D 52250
26.9%
C 22183
 
11.4%
E 10591
 
5.4%
B 557
 
0.3%

cat99
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
P
82100 
T
74997 
R
10642 
D
9129 
S
 
7279
Other values (11)
10445 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT
2nd rowT
3rd rowD
4th rowT
5th rowP

Common Values

ValueCountFrequency (%)
P 82100
42.2%
T 74997
38.5%
R 10642
 
5.5%
D 9129
 
4.7%
S 7279
 
3.7%
N 2983
 
1.5%
K 2808
 
1.4%
F 2790
 
1.4%
E 1067
 
0.5%
C 327
 
0.2%
Other values (6) 470
 
0.2%

Length

2023-09-24T22:00:30.109542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p 82100
42.2%
t 74997
38.5%
r 10642
 
5.5%
d 9129
 
4.7%
s 7279
 
3.7%
n 2983
 
1.5%
k 2808
 
1.4%
f 2790
 
1.4%
e 1067
 
0.5%
c 327
 
0.2%
Other values (6) 470
 
0.2%

Most occurring characters

ValueCountFrequency (%)
P 82100
42.2%
T 74997
38.5%
R 10642
 
5.5%
D 9129
 
4.7%
S 7279
 
3.7%
N 2983
 
1.5%
K 2808
 
1.4%
F 2790
 
1.4%
E 1067
 
0.5%
C 327
 
0.2%
Other values (6) 470
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 82100
42.2%
T 74997
38.5%
R 10642
 
5.5%
D 9129
 
4.7%
S 7279
 
3.7%
N 2983
 
1.5%
K 2808
 
1.4%
F 2790
 
1.4%
E 1067
 
0.5%
C 327
 
0.2%
Other values (6) 470
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 82100
42.2%
T 74997
38.5%
R 10642
 
5.5%
D 9129
 
4.7%
S 7279
 
3.7%
N 2983
 
1.5%
K 2808
 
1.4%
F 2790
 
1.4%
E 1067
 
0.5%
C 327
 
0.2%
Other values (6) 470
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 82100
42.2%
T 74997
38.5%
R 10642
 
5.5%
D 9129
 
4.7%
S 7279
 
3.7%
N 2983
 
1.5%
K 2808
 
1.4%
F 2790
 
1.4%
E 1067
 
0.5%
C 327
 
0.2%
Other values (6) 470
 
0.2%

cat100
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
F
44435 
I
41287 
L
20625 
K
14242 
G
13351 
Other values (10)
60652 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowL
3rd rowL
4th rowI
5th rowF

Common Values

ValueCountFrequency (%)
F 44435
22.8%
I 41287
21.2%
L 20625
10.6%
K 14242
 
7.3%
G 13351
 
6.9%
J 12428
 
6.4%
H 11123
 
5.7%
A 9721
 
5.0%
N 7832
 
4.0%
B 6821
 
3.5%
Other values (5) 12727
 
6.5%

Length

2023-09-24T22:00:30.288629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f 44435
22.8%
i 41287
21.2%
l 20625
10.6%
k 14242
 
7.3%
g 13351
 
6.9%
j 12428
 
6.4%
h 11123
 
5.7%
a 9721
 
5.0%
n 7832
 
4.0%
b 6821
 
3.5%
Other values (5) 12727
 
6.5%

Most occurring characters

ValueCountFrequency (%)
F 44435
22.8%
I 41287
21.2%
L 20625
10.6%
K 14242
 
7.3%
G 13351
 
6.9%
J 12428
 
6.4%
H 11123
 
5.7%
A 9721
 
5.0%
N 7832
 
4.0%
B 6821
 
3.5%
Other values (5) 12727
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 44435
22.8%
I 41287
21.2%
L 20625
10.6%
K 14242
 
7.3%
G 13351
 
6.9%
J 12428
 
6.4%
H 11123
 
5.7%
A 9721
 
5.0%
N 7832
 
4.0%
B 6821
 
3.5%
Other values (5) 12727
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 44435
22.8%
I 41287
21.2%
L 20625
10.6%
K 14242
 
7.3%
G 13351
 
6.9%
J 12428
 
6.4%
H 11123
 
5.7%
A 9721
 
5.0%
N 7832
 
4.0%
B 6821
 
3.5%
Other values (5) 12727
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 44435
22.8%
I 41287
21.2%
L 20625
10.6%
K 14242
 
7.3%
G 13351
 
6.9%
J 12428
 
6.4%
H 11123
 
5.7%
A 9721
 
5.0%
N 7832
 
4.0%
B 6821
 
3.5%
Other values (5) 12727
 
6.5%

cat101
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
110253 
D
17771 
C
17522 
G
11310 
F
 
10493
Other values (14)
27243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowG
2nd rowF
3rd rowO
4th rowD
5th rowJ

Common Values

ValueCountFrequency (%)
A 110253
56.7%
D 17771
 
9.1%
C 17522
 
9.0%
G 11310
 
5.8%
F 10493
 
5.4%
J 7536
 
3.9%
I 6916
 
3.6%
M 3775
 
1.9%
L 3263
 
1.7%
Q 2839
 
1.5%
Other values (9) 2914
 
1.5%

Length

2023-09-24T22:00:30.468031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 110253
56.7%
d 17771
 
9.1%
c 17522
 
9.0%
g 11310
 
5.8%
f 10493
 
5.4%
j 7536
 
3.9%
i 6916
 
3.6%
m 3775
 
1.9%
l 3263
 
1.7%
q 2839
 
1.5%
Other values (9) 2914
 
1.5%

Most occurring characters

ValueCountFrequency (%)
A 110253
56.7%
D 17771
 
9.1%
C 17522
 
9.0%
G 11310
 
5.8%
F 10493
 
5.4%
J 7536
 
3.9%
I 6916
 
3.6%
M 3775
 
1.9%
L 3263
 
1.7%
Q 2839
 
1.5%
Other values (9) 2914
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 110253
56.7%
D 17771
 
9.1%
C 17522
 
9.0%
G 11310
 
5.8%
F 10493
 
5.4%
J 7536
 
3.9%
I 6916
 
3.6%
M 3775
 
1.9%
L 3263
 
1.7%
Q 2839
 
1.5%
Other values (9) 2914
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 110253
56.7%
D 17771
 
9.1%
C 17522
 
9.0%
G 11310
 
5.8%
F 10493
 
5.4%
J 7536
 
3.9%
I 6916
 
3.6%
M 3775
 
1.9%
L 3263
 
1.7%
Q 2839
 
1.5%
Other values (9) 2914
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 110253
56.7%
D 17771
 
9.1%
C 17522
 
9.0%
G 11310
 
5.8%
F 10493
 
5.4%
J 7536
 
3.9%
I 6916
 
3.6%
M 3775
 
1.9%
L 3263
 
1.7%
Q 2839
 
1.5%
Other values (9) 2914
 
1.5%

cat102
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
183169 
B
 
5309
C
 
5120
E
 
500
D
 
464
Other values (4)
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 183169
94.1%
B 5309
 
2.7%
C 5120
 
2.6%
E 500
 
0.3%
D 464
 
0.2%
G 16
 
< 0.1%
F 12
 
< 0.1%
H 1
 
< 0.1%
J 1
 
< 0.1%

Length

2023-09-24T22:00:30.655887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:30.853416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 183169
94.1%
b 5309
 
2.7%
c 5120
 
2.6%
e 500
 
0.3%
d 464
 
0.2%
g 16
 
< 0.1%
f 12
 
< 0.1%
h 1
 
< 0.1%
j 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 183169
94.1%
B 5309
 
2.7%
C 5120
 
2.6%
E 500
 
0.3%
D 464
 
0.2%
G 16
 
< 0.1%
F 12
 
< 0.1%
H 1
 
< 0.1%
J 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 183169
94.1%
B 5309
 
2.7%
C 5120
 
2.6%
E 500
 
0.3%
D 464
 
0.2%
G 16
 
< 0.1%
F 12
 
< 0.1%
H 1
 
< 0.1%
J 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 183169
94.1%
B 5309
 
2.7%
C 5120
 
2.6%
E 500
 
0.3%
D 464
 
0.2%
G 16
 
< 0.1%
F 12
 
< 0.1%
H 1
 
< 0.1%
J 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 183169
94.1%
B 5309
 
2.7%
C 5120
 
2.6%
E 500
 
0.3%
D 464
 
0.2%
G 16
 
< 0.1%
F 12
 
< 0.1%
H 1
 
< 0.1%
J 1
 
< 0.1%

cat103
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
127844 
B
34429 
C
17063 
D
 
8068
E
 
4638
Other values (8)
 
2550

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 127844
65.7%
B 34429
 
17.7%
C 17063
 
8.8%
D 8068
 
4.1%
E 4638
 
2.4%
F 1590
 
0.8%
G 571
 
0.3%
H 197
 
0.1%
I 114
 
0.1%
J 47
 
< 0.1%
Other values (3) 31
 
< 0.1%

Length

2023-09-24T22:00:31.038630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 127844
65.7%
b 34429
 
17.7%
c 17063
 
8.8%
d 8068
 
4.1%
e 4638
 
2.4%
f 1590
 
0.8%
g 571
 
0.3%
h 197
 
0.1%
i 114
 
0.1%
j 47
 
< 0.1%
Other values (3) 31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 127844
65.7%
B 34429
 
17.7%
C 17063
 
8.8%
D 8068
 
4.1%
E 4638
 
2.4%
F 1590
 
0.8%
G 571
 
0.3%
H 197
 
0.1%
I 114
 
0.1%
J 47
 
< 0.1%
Other values (3) 31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 127844
65.7%
B 34429
 
17.7%
C 17063
 
8.8%
D 8068
 
4.1%
E 4638
 
2.4%
F 1590
 
0.8%
G 571
 
0.3%
H 197
 
0.1%
I 114
 
0.1%
J 47
 
< 0.1%
Other values (3) 31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 127844
65.7%
B 34429
 
17.7%
C 17063
 
8.8%
D 8068
 
4.1%
E 4638
 
2.4%
F 1590
 
0.8%
G 571
 
0.3%
H 197
 
0.1%
I 114
 
0.1%
J 47
 
< 0.1%
Other values (3) 31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 127844
65.7%
B 34429
 
17.7%
C 17063
 
8.8%
D 8068
 
4.1%
E 4638
 
2.4%
F 1590
 
0.8%
G 571
 
0.3%
H 197
 
0.1%
I 114
 
0.1%
J 47
 
< 0.1%
Other values (3) 31
 
< 0.1%

cat104
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
E
44321 
G
42004 
D
28587 
F
19857 
H
17767 
Other values (12)
42056 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowE
3rd rowE
4th rowE
5th rowD

Common Values

ValueCountFrequency (%)
E 44321
22.8%
G 42004
21.6%
D 28587
14.7%
F 19857
10.2%
H 17767
9.1%
K 14771
 
7.6%
I 11265
 
5.8%
C 7181
 
3.7%
L 3601
 
1.9%
J 3248
 
1.7%
Other values (7) 1990
 
1.0%

Length

2023-09-24T22:00:31.218074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e 44321
22.8%
g 42004
21.6%
d 28587
14.7%
f 19857
10.2%
h 17767
9.1%
k 14771
 
7.6%
i 11265
 
5.8%
c 7181
 
3.7%
l 3601
 
1.9%
j 3248
 
1.7%
Other values (7) 1990
 
1.0%

Most occurring characters

ValueCountFrequency (%)
E 44321
22.8%
G 42004
21.6%
D 28587
14.7%
F 19857
10.2%
H 17767
9.1%
K 14771
 
7.6%
I 11265
 
5.8%
C 7181
 
3.7%
L 3601
 
1.9%
J 3248
 
1.7%
Other values (7) 1990
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 44321
22.8%
G 42004
21.6%
D 28587
14.7%
F 19857
10.2%
H 17767
9.1%
K 14771
 
7.6%
I 11265
 
5.8%
C 7181
 
3.7%
L 3601
 
1.9%
J 3248
 
1.7%
Other values (7) 1990
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 44321
22.8%
G 42004
21.6%
D 28587
14.7%
F 19857
10.2%
H 17767
9.1%
K 14771
 
7.6%
I 11265
 
5.8%
C 7181
 
3.7%
L 3601
 
1.9%
J 3248
 
1.7%
Other values (7) 1990
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 44321
22.8%
G 42004
21.6%
D 28587
14.7%
F 19857
10.2%
H 17767
9.1%
K 14771
 
7.6%
I 11265
 
5.8%
C 7181
 
3.7%
L 3601
 
1.9%
J 3248
 
1.7%
Other values (7) 1990
 
1.0%

cat105
Categorical

IMBALANCE 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
E
79021 
F
65010 
G
21325 
D
12589 
H
11609 
Other values (15)
 
5038

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowE
2nd rowE
3rd rowF
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 79021
40.6%
F 65010
33.4%
G 21325
 
11.0%
D 12589
 
6.5%
H 11609
 
6.0%
I 3033
 
1.6%
J 712
 
0.4%
K 501
 
0.3%
C 280
 
0.1%
M 179
 
0.1%
Other values (10) 333
 
0.2%

Length

2023-09-24T22:00:31.395291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e 79021
40.6%
f 65010
33.4%
g 21325
 
11.0%
d 12589
 
6.5%
h 11609
 
6.0%
i 3033
 
1.6%
j 712
 
0.4%
k 501
 
0.3%
c 280
 
0.1%
m 179
 
0.1%
Other values (10) 333
 
0.2%

Most occurring characters

ValueCountFrequency (%)
E 79021
40.6%
F 65010
33.4%
G 21325
 
11.0%
D 12589
 
6.5%
H 11609
 
6.0%
I 3033
 
1.6%
J 712
 
0.4%
K 501
 
0.3%
C 280
 
0.1%
M 179
 
0.1%
Other values (10) 333
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 79021
40.6%
F 65010
33.4%
G 21325
 
11.0%
D 12589
 
6.5%
H 11609
 
6.0%
I 3033
 
1.6%
J 712
 
0.4%
K 501
 
0.3%
C 280
 
0.1%
M 179
 
0.1%
Other values (10) 333
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 79021
40.6%
F 65010
33.4%
G 21325
 
11.0%
D 12589
 
6.5%
H 11609
 
6.0%
I 3033
 
1.6%
J 712
 
0.4%
K 501
 
0.3%
C 280
 
0.1%
M 179
 
0.1%
Other values (10) 333
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 79021
40.6%
F 65010
33.4%
G 21325
 
11.0%
D 12589
 
6.5%
H 11609
 
6.0%
I 3033
 
1.6%
J 712
 
0.4%
K 501
 
0.3%
C 280
 
0.1%
M 179
 
0.1%
Other values (10) 333
 
0.2%

cat106
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
G
48710 
H
38958 
F
37311 
I
22165 
J
18894 
Other values (12)
28554 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowI
3rd rowH
4th rowI
5th rowK

Common Values

ValueCountFrequency (%)
G 48710
25.0%
H 38958
20.0%
F 37311
19.2%
I 22165
11.4%
J 18894
 
9.7%
E 13450
 
6.9%
K 8242
 
4.2%
L 3085
 
1.6%
D 2000
 
1.0%
M 1215
 
0.6%
Other values (7) 562
 
0.3%

Length

2023-09-24T22:00:31.575284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g 48710
25.0%
h 38958
20.0%
f 37311
19.2%
i 22165
11.4%
j 18894
 
9.7%
e 13450
 
6.9%
k 8242
 
4.2%
l 3085
 
1.6%
d 2000
 
1.0%
m 1215
 
0.6%
Other values (7) 562
 
0.3%

Most occurring characters

ValueCountFrequency (%)
G 48710
25.0%
H 38958
20.0%
F 37311
19.2%
I 22165
11.4%
J 18894
 
9.7%
E 13450
 
6.9%
K 8242
 
4.2%
L 3085
 
1.6%
D 2000
 
1.0%
M 1215
 
0.6%
Other values (7) 562
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 48710
25.0%
H 38958
20.0%
F 37311
19.2%
I 22165
11.4%
J 18894
 
9.7%
E 13450
 
6.9%
K 8242
 
4.2%
L 3085
 
1.6%
D 2000
 
1.0%
M 1215
 
0.6%
Other values (7) 562
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 48710
25.0%
H 38958
20.0%
F 37311
19.2%
I 22165
11.4%
J 18894
 
9.7%
E 13450
 
6.9%
K 8242
 
4.2%
L 3085
 
1.6%
D 2000
 
1.0%
M 1215
 
0.6%
Other values (7) 562
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 48710
25.0%
H 38958
20.0%
F 37311
19.2%
I 22165
11.4%
J 18894
 
9.7%
E 13450
 
6.9%
K 8242
 
4.2%
L 3085
 
1.6%
D 2000
 
1.0%
M 1215
 
0.6%
Other values (7) 562
 
0.3%

cat107
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
F
48875 
G
29526 
H
24207 
J
23200 
K
20906 
Other values (15)
47878 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJ
2nd rowK
3rd rowF
4th rowK
5th rowG

Common Values

ValueCountFrequency (%)
F 48875
25.1%
G 29526
15.2%
H 24207
12.4%
J 23200
11.9%
K 20906
10.7%
I 20756
10.7%
E 12933
 
6.6%
L 7177
 
3.7%
D 3337
 
1.7%
M 2132
 
1.1%
Other values (10) 1543
 
0.8%

Length

2023-09-24T22:00:31.757024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f 48875
25.1%
g 29526
15.2%
h 24207
12.4%
j 23200
11.9%
k 20906
10.7%
i 20756
10.7%
e 12933
 
6.6%
l 7177
 
3.7%
d 3337
 
1.7%
m 2132
 
1.1%
Other values (10) 1543
 
0.8%

Most occurring characters

ValueCountFrequency (%)
F 48875
25.1%
G 29526
15.2%
H 24207
12.4%
J 23200
11.9%
K 20906
10.7%
I 20756
10.7%
E 12933
 
6.6%
L 7177
 
3.7%
D 3337
 
1.7%
M 2132
 
1.1%
Other values (10) 1543
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 48875
25.1%
G 29526
15.2%
H 24207
12.4%
J 23200
11.9%
K 20906
10.7%
I 20756
10.7%
E 12933
 
6.6%
L 7177
 
3.7%
D 3337
 
1.7%
M 2132
 
1.1%
Other values (10) 1543
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 48875
25.1%
G 29526
15.2%
H 24207
12.4%
J 23200
11.9%
K 20906
10.7%
I 20756
10.7%
E 12933
 
6.6%
L 7177
 
3.7%
D 3337
 
1.7%
M 2132
 
1.1%
Other values (10) 1543
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 48875
25.1%
G 29526
15.2%
H 24207
12.4%
J 23200
11.9%
K 20906
10.7%
I 20756
10.7%
E 12933
 
6.6%
L 7177
 
3.7%
D 3337
 
1.7%
M 2132
 
1.1%
Other values (10) 1543
 
0.8%

cat108
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
67732 
K
43860 
G
22117 
D
19752 
F
10591 
Other values (6)
30540 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowK
3rd rowA
4th rowK
5th rowB

Common Values

ValueCountFrequency (%)
B 67732
34.8%
K 43860
22.5%
G 22117
 
11.4%
D 19752
 
10.2%
F 10591
 
5.4%
A 9617
 
4.9%
E 8232
 
4.2%
I 7479
 
3.8%
H 4459
 
2.3%
C 534
 
0.3%

Length

2023-09-24T22:00:31.952477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b 67732
34.8%
k 43860
22.5%
g 22117
 
11.4%
d 19752
 
10.2%
f 10591
 
5.4%
a 9617
 
4.9%
e 8232
 
4.2%
i 7479
 
3.8%
h 4459
 
2.3%
c 534
 
0.3%

Most occurring characters

ValueCountFrequency (%)
B 67732
34.8%
K 43860
22.5%
G 22117
 
11.4%
D 19752
 
10.2%
F 10591
 
5.4%
A 9617
 
4.9%
E 8232
 
4.2%
I 7479
 
3.8%
H 4459
 
2.3%
C 534
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 67732
34.8%
K 43860
22.5%
G 22117
 
11.4%
D 19752
 
10.2%
F 10591
 
5.4%
A 9617
 
4.9%
E 8232
 
4.2%
I 7479
 
3.8%
H 4459
 
2.3%
C 534
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 67732
34.8%
K 43860
22.5%
G 22117
 
11.4%
D 19752
 
10.2%
F 10591
 
5.4%
A 9617
 
4.9%
E 8232
 
4.2%
I 7479
 
3.8%
H 4459
 
2.3%
C 534
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 67732
34.8%
K 43860
22.5%
G 22117
 
11.4%
D 19752
 
10.2%
F 10591
 
5.4%
A 9617
 
4.9%
E 8232
 
4.2%
I 7479
 
3.8%
H 4459
 
2.3%
C 534
 
0.3%

cat109
Text

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:32.263277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9666533
Min length1

Characters and Unicode

Total characters382695
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowBU
2nd rowBI
3rd rowAB
4th rowBI
5th rowH
ValueCountFrequency (%)
bi 157968
81.2%
ab 22698
 
11.7%
bu 3242
 
1.7%
k 3103
 
1.6%
g 1409
 
0.7%
bq 1100
 
0.6%
n 477
 
0.2%
m 456
 
0.2%
bo 340
 
0.2%
bh 271
 
0.1%
Other values (74) 3528
 
1.8%
2023-09-24T22:00:32.728599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 186016
48.6%
I 158199
41.3%
A 24122
 
6.3%
U 3345
 
0.9%
K 3133
 
0.8%
G 1421
 
0.4%
Q 1227
 
0.3%
C 714
 
0.2%
L 555
 
0.1%
M 511
 
0.1%
Other values (17) 3452
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 382463
99.9%
Space Separator 232
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 186016
48.6%
I 158199
41.4%
A 24122
 
6.3%
U 3345
 
0.9%
K 3133
 
0.8%
G 1421
 
0.4%
Q 1227
 
0.3%
C 714
 
0.2%
L 555
 
0.1%
M 511
 
0.1%
Other values (16) 3220
 
0.8%
Space Separator
ValueCountFrequency (%)
232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 382463
99.9%
Common 232
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 186016
48.6%
I 158199
41.4%
A 24122
 
6.3%
U 3345
 
0.9%
K 3133
 
0.8%
G 1421
 
0.4%
Q 1227
 
0.3%
C 714
 
0.2%
L 555
 
0.1%
M 511
 
0.1%
Other values (16) 3220
 
0.8%
Common
ValueCountFrequency (%)
232
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 186016
48.6%
I 158199
41.3%
A 24122
 
6.3%
U 3345
 
0.9%
K 3133
 
0.8%
G 1421
 
0.4%
Q 1227
 
0.3%
C 714
 
0.2%
L 555
 
0.1%
M 511
 
0.1%
Other values (17) 3452
 
0.9%

cat110
Text

Distinct146
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:33.163996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9472949
Min length1

Characters and Unicode

Total characters378928
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowBC
2nd rowCQ
3rd rowDK
4th rowCS
5th rowC
ValueCountFrequency (%)
cl 26123
13.4%
eg 25441
13.1%
cs 25428
13.1%
eb 22120
11.4%
co 18076
9.3%
bt 16902
8.7%
el 9555
 
4.9%
bc 4254
 
2.2%
dw 3772
 
1.9%
cq 3371
 
1.7%
Other values (121) 39550
20.3%
2023-09-24T22:00:33.792510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 87682
23.1%
E 61357
16.2%
B 48795
12.9%
L 36240
9.6%
S 27428
 
7.2%
G 25869
 
6.8%
O 18259
 
4.8%
T 17539
 
4.6%
D 11524
 
3.0%
A 10407
 
2.7%
Other values (16) 33828
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 378573
99.9%
Space Separator 355
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 87682
23.2%
E 61357
16.2%
B 48795
12.9%
L 36240
9.6%
S 27428
 
7.2%
G 25869
 
6.8%
O 18259
 
4.8%
T 17539
 
4.6%
D 11524
 
3.0%
A 10407
 
2.7%
Other values (15) 33473
 
8.8%
Space Separator
ValueCountFrequency (%)
355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378573
99.9%
Common 355
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 87682
23.2%
E 61357
16.2%
B 48795
12.9%
L 36240
9.6%
S 27428
 
7.2%
G 25869
 
6.8%
O 18259
 
4.8%
T 17539
 
4.6%
D 11524
 
3.0%
A 10407
 
2.7%
Other values (15) 33473
 
8.8%
Common
ValueCountFrequency (%)
355
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 87682
23.1%
E 61357
16.2%
B 48795
12.9%
L 36240
9.6%
S 27428
 
7.2%
G 25869
 
6.8%
O 18259
 
4.8%
T 17539
 
4.6%
D 11524
 
3.0%
A 10407
 
2.7%
Other values (16) 33828
 
8.9%

cat111
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
128395 
C
32401 
E
14682 
G
 
7039
A
 
4277
Other values (22)
 
7798

Length

Max length2
Median length1
Mean length1.0322418
Min length1

Characters and Unicode

Total characters200866
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowA
3rd rowA
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
A 128395
66.0%
C 32401
 
16.7%
E 14682
 
7.5%
G 7039
 
3.6%
A 4277
 
2.2%
I 3578
 
1.8%
K 1353
 
0.7%
C 1030
 
0.5%
E 522
 
0.3%
M 473
 
0.2%
Other values (17) 842
 
0.4%

Length

2023-09-24T22:00:34.055076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 132672
68.2%
c 33431
 
17.2%
e 15204
 
7.8%
g 7270
 
3.7%
i 3721
 
1.9%
k 1393
 
0.7%
m 494
 
0.3%
o 227
 
0.1%
q 93
 
< 0.1%
s 39
 
< 0.1%
Other values (6) 48
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 132672
66.1%
C 33431
 
16.6%
E 15204
 
7.6%
G 7270
 
3.6%
6274
 
3.1%
I 3721
 
1.9%
K 1393
 
0.7%
M 494
 
0.2%
O 227
 
0.1%
Q 93
 
< 0.1%
Other values (7) 87
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
96.9%
Space Separator 6274
 
3.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 132672
68.2%
C 33431
 
17.2%
E 15204
 
7.8%
G 7270
 
3.7%
I 3721
 
1.9%
K 1393
 
0.7%
M 494
 
0.3%
O 227
 
0.1%
Q 93
 
< 0.1%
S 39
 
< 0.1%
Other values (6) 48
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
96.9%
Common 6274
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 132672
68.2%
C 33431
 
17.2%
E 15204
 
7.8%
G 7270
 
3.7%
I 3721
 
1.9%
K 1393
 
0.7%
M 494
 
0.3%
O 227
 
0.1%
Q 93
 
< 0.1%
S 39
 
< 0.1%
Other values (6) 48
 
< 0.1%
Common
ValueCountFrequency (%)
6274
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 132672
66.1%
C 33431
 
16.6%
E 15204
 
7.6%
G 7270
 
3.6%
6274
 
3.1%
I 3721
 
1.9%
K 1393
 
0.7%
M 494
 
0.2%
O 227
 
0.1%
Q 93
 
< 0.1%
Other values (7) 87
 
< 0.1%

cat112
Text

Distinct76
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:34.332600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.5253248
Min length1

Characters and Unicode

Total characters296816
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAS
2nd rowAV
3rd rowC
4th rowN
5th rowY
ValueCountFrequency (%)
e 25967
13.3%
ah 19329
 
9.9%
as 18235
 
9.4%
j 16773
 
8.6%
af 9663
 
5.0%
an 9438
 
4.9%
n 8723
 
4.5%
u 8614
 
4.4%
av 7365
 
3.8%
ak 6949
 
3.6%
Other values (41) 63536
32.7%
2023-09-24T22:00:34.836442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 102992
34.7%
E 26822
 
9.0%
S 22564
 
7.6%
H 19895
 
6.7%
N 18161
 
6.1%
J 16919
 
5.7%
K 13223
 
4.5%
F 12910
 
4.3%
U 9065
 
3.1%
V 8074
 
2.7%
Other values (16) 46191
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 293789
99.0%
Space Separator 3027
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 102992
35.1%
E 26822
 
9.1%
S 22564
 
7.7%
H 19895
 
6.8%
N 18161
 
6.2%
J 16919
 
5.8%
K 13223
 
4.5%
F 12910
 
4.4%
U 9065
 
3.1%
V 8074
 
2.7%
Other values (15) 43164
14.7%
Space Separator
ValueCountFrequency (%)
3027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 293789
99.0%
Common 3027
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 102992
35.1%
E 26822
 
9.1%
S 22564
 
7.7%
H 19895
 
6.8%
N 18161
 
6.2%
J 16919
 
5.8%
K 13223
 
4.5%
F 12910
 
4.4%
U 9065
 
3.1%
V 8074
 
2.7%
Other values (15) 43164
14.7%
Common
ValueCountFrequency (%)
3027
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 296816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 102992
34.7%
E 26822
 
9.0%
S 22564
 
7.6%
H 19895
 
6.7%
N 18161
 
6.1%
J 16919
 
5.7%
K 13223
 
4.5%
F 12910
 
4.3%
U 9065
 
3.1%
V 8074
 
2.7%
Other values (16) 46191
15.6%

cat113
Text

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:35.157068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.6364445
Min length1

Characters and Unicode

Total characters318439
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowS
2nd rowBM
3rd rowAF
4th rowAE
5th rowBM
ValueCountFrequency (%)
bm 27062
 
13.9%
ae 22781
 
11.7%
l 13494
 
6.9%
ax 13091
 
6.7%
y 11711
 
6.0%
k 8001
 
4.1%
s 7259
 
3.7%
x 7254
 
3.7%
af 6302
 
3.2%
an 5372
 
2.8%
Other values (51) 72265
37.1%
2023-09-24T22:00:35.671568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 82556
25.9%
B 42463
13.3%
M 30107
 
9.5%
E 22791
 
7.2%
X 20345
 
6.4%
L 13500
 
4.2%
K 12346
 
3.9%
Y 12195
 
3.8%
N 11925
 
3.7%
S 11296
 
3.5%
Other values (16) 58915
18.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 316128
99.3%
Space Separator 2311
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 82556
26.1%
B 42463
13.4%
M 30107
 
9.5%
E 22791
 
7.2%
X 20345
 
6.4%
L 13500
 
4.3%
K 12346
 
3.9%
Y 12195
 
3.9%
N 11925
 
3.8%
S 11296
 
3.6%
Other values (15) 56604
17.9%
Space Separator
ValueCountFrequency (%)
2311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316128
99.3%
Common 2311
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 82556
26.1%
B 42463
13.4%
M 30107
 
9.5%
E 22791
 
7.2%
X 20345
 
6.4%
L 13500
 
4.3%
K 12346
 
3.9%
Y 12195
 
3.9%
N 11925
 
3.8%
S 11296
 
3.6%
Other values (15) 56604
17.9%
Common
ValueCountFrequency (%)
2311
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 318439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 82556
25.9%
B 42463
13.3%
M 30107
 
9.5%
E 22791
 
7.2%
X 20345
 
6.4%
L 13500
 
4.2%
K 12346
 
3.9%
Y 12195
 
3.8%
N 11925
 
3.7%
S 11296
 
3.5%
Other values (16) 58915
18.5%

cat114
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
A
136134 
C
17319 
E
17026 
J
 
8469
F
 
8142
Other values (14)
 
7502

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 136134
70.0%
C 17319
 
8.9%
E 17026
 
8.7%
J 8469
 
4.4%
F 8142
 
4.2%
N 2536
 
1.3%
I 2528
 
1.3%
R 946
 
0.5%
L 895
 
0.5%
U 253
 
0.1%
Other values (9) 344
 
0.2%

Length

2023-09-24T22:00:35.904631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 136134
70.0%
c 17319
 
8.9%
e 17026
 
8.7%
j 8469
 
4.4%
f 8142
 
4.2%
n 2536
 
1.3%
i 2528
 
1.3%
r 946
 
0.5%
l 895
 
0.5%
u 253
 
0.1%
Other values (9) 344
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 136134
70.0%
C 17319
 
8.9%
E 17026
 
8.7%
J 8469
 
4.4%
F 8142
 
4.2%
N 2536
 
1.3%
I 2528
 
1.3%
R 946
 
0.5%
L 895
 
0.5%
U 253
 
0.1%
Other values (9) 344
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 136134
70.0%
C 17319
 
8.9%
E 17026
 
8.7%
J 8469
 
4.4%
F 8142
 
4.2%
N 2536
 
1.3%
I 2528
 
1.3%
R 946
 
0.5%
L 895
 
0.5%
U 253
 
0.1%
Other values (9) 344
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 136134
70.0%
C 17319
 
8.9%
E 17026
 
8.7%
J 8469
 
4.4%
F 8142
 
4.2%
N 2536
 
1.3%
I 2528
 
1.3%
R 946
 
0.5%
L 895
 
0.5%
U 253
 
0.1%
Other values (9) 344
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 136134
70.0%
C 17319
 
8.9%
E 17026
 
8.7%
J 8469
 
4.4%
F 8142
 
4.2%
N 2536
 
1.3%
I 2528
 
1.3%
R 946
 
0.5%
L 895
 
0.5%
U 253
 
0.1%
Other values (9) 344
 
0.2%

cat115
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
K
45372 
O
27717 
J
24678 
N
23185 
P
22274 
Other values (18)
51366 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters194592
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowO
2nd rowO
3rd rowI
4th rowO
5th rowK

Common Values

ValueCountFrequency (%)
K 45372
23.3%
O 27717
14.2%
J 24678
12.7%
N 23185
11.9%
P 22274
11.4%
L 16632
 
8.5%
M 12882
 
6.6%
Q 8498
 
4.4%
I 7312
 
3.8%
H 2881
 
1.5%
Other values (13) 3161
 
1.6%

Length

2023-09-24T22:00:36.135940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
k 45372
23.3%
o 27717
14.2%
j 24678
12.7%
n 23185
11.9%
p 22274
11.4%
l 16632
 
8.5%
m 12882
 
6.6%
q 8498
 
4.4%
i 7312
 
3.8%
h 2881
 
1.5%
Other values (13) 3161
 
1.6%

Most occurring characters

ValueCountFrequency (%)
K 45372
23.3%
O 27717
14.2%
J 24678
12.7%
N 23185
11.9%
P 22274
11.4%
L 16632
 
8.5%
M 12882
 
6.6%
Q 8498
 
4.4%
I 7312
 
3.8%
H 2881
 
1.5%
Other values (13) 3161
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 194592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 45372
23.3%
O 27717
14.2%
J 24678
12.7%
N 23185
11.9%
P 22274
11.4%
L 16632
 
8.5%
M 12882
 
6.6%
Q 8498
 
4.4%
I 7312
 
3.8%
H 2881
 
1.5%
Other values (13) 3161
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 194592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 45372
23.3%
O 27717
14.2%
J 24678
12.7%
N 23185
11.9%
P 22274
11.4%
L 16632
 
8.5%
M 12882
 
6.6%
Q 8498
 
4.4%
I 7312
 
3.8%
H 2881
 
1.5%
Other values (13) 3161
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 45372
23.3%
O 27717
14.2%
J 24678
12.7%
N 23185
11.9%
P 22274
11.4%
L 16632
 
8.5%
M 12882
 
6.6%
Q 8498
 
4.4%
I 7312
 
3.8%
H 2881
 
1.5%
Other values (13) 3161
 
1.6%

cat116
Text

Distinct330
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:36.756698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9983658
Min length1

Characters and Unicode

Total characters388866
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)< 0.1%

Sample

1st rowLB
2nd rowDP
3rd rowGK
4th rowDJ
5th rowCK
ValueCountFrequency (%)
hk 21749
 
11.2%
dj 20885
 
10.7%
ck 10518
 
5.4%
dp 9528
 
4.9%
gs 9040
 
4.6%
cr 7092
 
3.6%
hx 5819
 
3.0%
dc 4721
 
2.4%
hg 4579
 
2.4%
ie 4377
 
2.2%
Other values (316) 96284
49.5%
2023-09-24T22:00:37.878322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
H 54014
13.9%
K 44754
11.5%
D 44721
11.5%
C 38702
10.0%
G 30473
 
7.8%
J 27133
 
7.0%
L 20818
 
5.4%
I 14808
 
3.8%
B 11739
 
3.0%
P 10914
 
2.8%
Other values (16) 90790
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 388857
> 99.9%
Space Separator 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 54014
13.9%
K 44754
11.5%
D 44721
11.5%
C 38702
10.0%
G 30473
 
7.8%
J 27133
 
7.0%
L 20818
 
5.4%
I 14808
 
3.8%
B 11739
 
3.0%
P 10914
 
2.8%
Other values (15) 90781
23.3%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 388857
> 99.9%
Common 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 54014
13.9%
K 44754
11.5%
D 44721
11.5%
C 38702
10.0%
G 30473
 
7.8%
J 27133
 
7.0%
L 20818
 
5.4%
I 14808
 
3.8%
B 11739
 
3.0%
P 10914
 
2.8%
Other values (15) 90781
23.3%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 54014
13.9%
K 44754
11.5%
D 44721
11.5%
C 38702
10.0%
G 30473
 
7.8%
J 27133
 
7.0%
L 20818
 
5.4%
I 14808
 
3.8%
B 11739
 
3.0%
P 10914
 
2.8%
Other values (16) 90790
23.3%

cont1
Real number (ℝ)

HIGH CORRELATION 

Distinct647
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4937922
Minimum1.6 × 10-5
Maximum0.984975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:38.150448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6 × 10-5
5-th percentile0.232722
Q10.34609
median0.475784
Q30.623912
95-th percentile0.893784
Maximum0.984975
Range0.984959
Interquartile range (IQR)0.277822

Descriptive statistics

Standard deviation0.1876362
Coefficient of variation (CV)0.3799902
Kurtosis-0.10131671
Mean0.4937922
Median Absolute Deviation (MAD)0.134925
Skewness0.51626382
Sum96088.012
Variance0.035207343
MonotonicityNot monotonic
2023-09-24T22:00:38.398833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.475784 6057
 
3.1%
0.503312 3367
 
1.7%
0.325401 3086
 
1.6%
0.54667 2985
 
1.5%
0.330514 2828
 
1.5%
0.484469 2797
 
1.4%
0.480125 2635
 
1.4%
0.894333 2324
 
1.2%
0.465671 2166
 
1.1%
0.629339 2125
 
1.1%
Other values (637) 164222
84.4%
ValueCountFrequency (%)
1.6 × 10-5232
0.1%
0.007274 32
 
< 0.1%
0.016235 3
 
< 0.1%
0.048643 15
 
< 0.1%
0.057987 167
0.1%
0.058944 7
 
< 0.1%
0.059591 4
 
< 0.1%
0.061571 21
 
< 0.1%
0.062925 1
 
< 0.1%
0.067156 1
 
< 0.1%
ValueCountFrequency (%)
0.984975 2
 
< 0.1%
0.984452 7
 
< 0.1%
0.981552 9
 
< 0.1%
0.980802 40
< 0.1%
0.980582 2
 
< 0.1%
0.977873 69
< 0.1%
0.977747 37
< 0.1%
0.976847 3
 
< 0.1%
0.970149 1
 
< 0.1%
0.968775 1
 
< 0.1%

cont2
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50709265
Minimum0.001149
Maximum0.862654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:38.617626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001149
5-th percentile0.15999
Q10.358319
median0.555782
Q30.681761
95-th percentile0.785784
Maximum0.862654
Range0.861505
Interquartile range (IQR)0.323442

Descriptive statistics

Standard deviation0.20714352
Coefficient of variation (CV)0.40849245
Kurtosis-0.89522283
Mean0.50709265
Median Absolute Deviation (MAD)0.181286
Skewness-0.31022995
Sum98676.172
Variance0.042908437
MonotonicityNot monotonic
2023-09-24T22:00:38.827873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.555782 21657
11.1%
0.620805 20891
10.7%
0.488789 19365
10.0%
0.737068 18024
9.3%
0.422197 17538
9.0%
0.785784 15956
8.2%
0.681761 15187
7.8%
0.358319 14866
7.6%
0.299102 11984
6.2%
0.245921 9718
 
5.0%
Other values (23) 29406
15.1%
ValueCountFrequency (%)
0.001149 2
 
< 0.1%
0.001503 4
 
< 0.1%
0.001966 6
 
< 0.1%
0.002571 8
 
< 0.1%
0.003362 12
 
< 0.1%
0.004394 28
 
< 0.1%
0.005742 27
 
< 0.1%
0.007501 35
< 0.1%
0.009792 54
< 0.1%
0.012775 72
< 0.1%
ValueCountFrequency (%)
0.862654 183
 
0.1%
0.827585 7259
 
3.7%
0.785784 15956
8.2%
0.737068 18024
9.3%
0.681761 15187
7.8%
0.620805 20891
10.7%
0.555782 21657
11.1%
0.488789 19365
10.0%
0.422197 17538
9.0%
0.358319 14866
7.6%

cont3
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49887533
Minimum0.002634
Maximum0.944251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:39.040316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002634
5-th percentile0.174588
Q10.336963
median0.527991
Q30.634224
95-th percentile0.819748
Maximum0.944251
Range0.941617
Interquartile range (IQR)0.297261

Descriptive statistics

Standard deviation0.20203269
Coefficient of variation (CV)0.40497631
Kurtosis-0.60525221
Mean0.49887533
Median Absolute Deviation (MAD)0.14587
Skewness-0.009833128
Sum97077.148
Variance0.040817207
MonotonicityNot monotonic
2023-09-24T22:00:39.309710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54977 17047
 
8.8%
0.484196 9493
 
4.9%
0.506105 9486
 
4.9%
0.527991 8510
 
4.4%
0.336963 7807
 
4.0%
0.61366 7730
 
4.0%
0.692825 7341
 
3.8%
0.440642 7072
 
3.6%
0.592681 6659
 
3.4%
0.57136 6412
 
3.3%
Other values (66) 107035
55.0%
ValueCountFrequency (%)
0.002634 85
 
< 0.1%
0.025153 1
 
< 0.1%
0.027395 6
 
< 0.1%
0.02983 7
 
< 0.1%
0.032474 50
 
< 0.1%
0.035344 11
 
< 0.1%
0.038457 43
 
< 0.1%
0.041833 259
0.1%
0.045491 5
 
< 0.1%
0.049453 107
0.1%
ValueCountFrequency (%)
0.944251 4811
2.5%
0.939453 514
 
0.3%
0.93427 3
 
< 0.1%
0.928678 39
 
< 0.1%
0.922649 64
 
< 0.1%
0.916157 101
 
0.1%
0.909173 529
 
0.3%
0.90167 135
 
0.1%
0.89362 115
 
0.1%
0.884994 214
 
0.1%

cont4
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49179097
Minimum0.176921
Maximum0.954297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:39.540723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.176921
5-th percentile0.208655
Q10.327354
median0.452887
Q30.652072
95-th percentile0.864586
Maximum0.954297
Range0.777376
Interquartile range (IQR)0.324718

Descriptive statistics

Standard deviation0.21127323
Coefficient of variation (CV)0.42959966
Kurtosis-0.96509259
Mean0.49179097
Median Absolute Deviation (MAD)0.161247
Skewness0.41642047
Sum95698.588
Variance0.044636379
MonotonicityNot monotonic
2023-09-24T22:00:39.797418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.373816 10056
 
5.2%
0.473202 8156
 
4.2%
0.327354 7077
 
3.6%
0.309621 6047
 
3.1%
0.452887 5582
 
2.9%
0.432728 5317
 
2.7%
0.412789 4876
 
2.5%
0.208655 4832
 
2.5%
0.534409 4752
 
2.4%
0.62377 4417
 
2.3%
Other values (102) 133480
68.6%
ValueCountFrequency (%)
0.176921 1149
 
0.6%
0.18295 2039
1.0%
0.189137 3131
1.6%
0.195483 2091
1.1%
0.201989 504
 
0.3%
0.208655 4832
2.5%
0.215482 3147
1.6%
0.222469 2018
1.0%
0.229617 3446
1.8%
0.236924 2322
1.2%
ValueCountFrequency (%)
0.954297 1
 
< 0.1%
0.952482 120
 
0.1%
0.950598 20
 
< 0.1%
0.948643 634
0.3%
0.946616 309
0.2%
0.944513 453
0.2%
0.942332 72
 
< 0.1%
0.940071 25
 
< 0.1%
0.935299 725
0.4%
0.932782 32
 
< 0.1%

cont5
Real number (ℝ)

Distinct141
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48738406
Minimum0.281143
Maximum0.983674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:40.015884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.281143
5-th percentile0.281143
Q10.281143
median0.422268
Q30.635304
95-th percentile0.885834
Maximum0.983674
Range0.702531
Interquartile range (IQR)0.354161

Descriptive statistics

Standard deviation0.20899755
Coefficient of variation (CV)0.42881492
Kurtosis-0.87659642
Mean0.48738406
Median Absolute Deviation (MAD)0.141125
Skewness0.68167059
Sum94841.038
Variance0.043679977
MonotonicityNot monotonic
2023-09-24T22:00:40.277065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.281143 53647
27.6%
0.422268 9289
 
4.8%
0.302678 8135
 
4.2%
0.372405 4793
 
2.5%
0.594196 3841
 
2.0%
0.78323 3606
 
1.9%
0.551723 3532
 
1.8%
0.534484 3375
 
1.7%
0.388783 3258
 
1.7%
0.491114 3194
 
1.6%
Other values (131) 97922
50.3%
ValueCountFrequency (%)
0.281143 53647
27.6%
0.288217 2268
 
1.2%
0.295397 2780
 
1.4%
0.302678 8135
 
4.2%
0.310061 1650
 
0.8%
0.317541 862
 
0.4%
0.325117 589
 
0.3%
0.332785 621
 
0.3%
0.340543 1642
 
0.8%
0.348388 2085
 
1.1%
ValueCountFrequency (%)
0.983674 2
 
< 0.1%
0.98252 61
< 0.1%
0.981914 1
 
< 0.1%
0.981286 45
< 0.1%
0.980637 4
 
< 0.1%
0.979967 6
 
< 0.1%
0.979273 2
 
< 0.1%
0.978556 2
 
< 0.1%
0.977049 8
 
< 0.1%
0.975438 3
 
< 0.1%

cont6
Real number (ℝ)

HIGH CORRELATION 

Distinct2573
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49085561
Minimum0.012683
Maximum0.997162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:40.520187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.012683
5-th percentile0.206854
Q10.33558
median0.440945
Q30.6542905
95-th percentile0.867697
Maximum0.997162
Range0.984479
Interquartile range (IQR)0.3187105

Descriptive statistics

Standard deviation0.20532886
Coefficient of variation (CV)0.41830806
Kurtosis-0.76018975
Mean0.49085561
Median Absolute Deviation (MAD)0.14213
Skewness0.46180705
Sum95516.575
Variance0.042159941
MonotonicityNot monotonic
2023-09-24T22:00:40.800196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3735 3146
 
1.6%
0.364464 2544
 
1.3%
0.240069 2171
 
1.1%
0.317735 1589
 
0.8%
0.201125 1578
 
0.8%
0.220686 1169
 
0.6%
0.252511 1111
 
0.6%
0.379574 1105
 
0.6%
0.220282 1085
 
0.6%
0.336105 1041
 
0.5%
Other values (2563) 178053
91.5%
ValueCountFrequency (%)
0.012683 224
0.1%
0.09723 28
 
< 0.1%
0.101117 1
 
< 0.1%
0.101331 4
 
< 0.1%
0.101868 6
 
< 0.1%
0.105031 2
 
< 0.1%
0.106588 2
 
< 0.1%
0.114328 1
 
< 0.1%
0.115885 3
 
< 0.1%
0.116368 2
 
< 0.1%
ValueCountFrequency (%)
0.997162 6
< 0.1%
0.996786 2
 
< 0.1%
0.99548 1
 
< 0.1%
0.989024 9
< 0.1%
0.98896 8
< 0.1%
0.988713 5
< 0.1%
0.987556 3
 
< 0.1%
0.987527 3
 
< 0.1%
0.98713 9
< 0.1%
0.986812 6
< 0.1%

cont7
Real number (ℝ)

HIGH CORRELATION 

Distinct5632
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48488854
Minimum0.069503
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:41.017739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.069503
5-th percentile0.262345
Q10.350175
median0.438285
Q30.590807
95-th percentile0.84915
Maximum1
Range0.930497
Interquartile range (IQR)0.240632

Descriptive statistics

Standard deviation0.17846606
Coefficient of variation (CV)0.36805584
Kurtosis0.051136379
Mean0.48488854
Median Absolute Deviation (MAD)0.110082
Skewness0.82699581
Sum94355.431
Variance0.031850134
MonotonicityNot monotonic
2023-09-24T22:00:41.261919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.347485 1240
 
0.6%
0.350175 1105
 
0.6%
0.381883 1072
 
0.6%
0.283691 1005
 
0.5%
0.362399 818
 
0.4%
0.359439 798
 
0.4%
0.39101 764
 
0.4%
0.401162 758
 
0.4%
0.279895 707
 
0.4%
0.409251 690
 
0.4%
Other values (5622) 185635
95.4%
ValueCountFrequency (%)
0.069503 314
0.2%
0.123055 1
 
< 0.1%
0.138701 1
 
< 0.1%
0.139493 1
 
< 0.1%
0.142276 1
 
< 0.1%
0.146545 2
 
< 0.1%
0.149244 1
 
< 0.1%
0.151514 1
 
< 0.1%
0.153619 2
 
< 0.1%
0.153876 2
 
< 0.1%
ValueCountFrequency (%)
1 31
< 0.1%
0.999999 5
 
< 0.1%
0.999998 1
 
< 0.1%
0.999997 1
 
< 0.1%
0.999996 3
 
< 0.1%
0.999993 1
 
< 0.1%
0.999988 1
 
< 0.1%
0.999987 2
 
< 0.1%
0.999984 1
 
< 0.1%
0.999975 2
 
< 0.1%

cont8
Real number (ℝ)

HIGH CORRELATION 

Distinct201
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48646762
Minimum0.23688
Maximum0.9802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:41.501358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.23688
5-th percentile0.24564
Q10.3128
median0.44106
Q30.62358
95-th percentile0.88693
Maximum0.9802
Range0.74332
Interquartile range (IQR)0.31078

Descriptive statistics

Standard deviation0.19941009
Coefficient of variation (CV)0.40991441
Kurtosis-0.54270943
Mean0.48646762
Median Absolute Deviation (MAD)0.14348
Skewness0.67657314
Sum94662.707
Variance0.039764382
MonotonicityNot monotonic
2023-09-24T22:00:41.760933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26847 11549
 
5.9%
0.36083 9565
 
4.9%
0.24564 8557
 
4.4%
0.27797 4696
 
2.4%
0.29758 4678
 
2.4%
0.58354 4381
 
2.3%
0.33906 4099
 
2.1%
0.34987 3610
 
1.9%
0.31796 3172
 
1.6%
0.3128 2868
 
1.5%
Other values (191) 137417
70.6%
ValueCountFrequency (%)
0.23688 1048
 
0.5%
0.24123 1634
 
0.8%
0.24564 8557
4.4%
0.2501 626
 
0.3%
0.25461 2117
 
1.1%
0.25918 1924
 
1.0%
0.2638 1030
 
0.5%
0.26847 11549
5.9%
0.2732 1451
 
0.7%
0.27797 4696
2.4%
ValueCountFrequency (%)
0.9802 273
0.1%
0.97973 496
0.3%
0.97925 28
 
< 0.1%
0.97875 6
 
< 0.1%
0.97825 1
 
< 0.1%
0.97774 1
 
< 0.1%
0.97721 1
 
< 0.1%
0.97667 1
 
< 0.1%
0.97556 109
 
0.1%
0.97498 156
 
0.1%

cont9
Real number (ℝ)

HIGH CORRELATION 

Distinct347
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48542486
Minimum8 × 10-5
Maximum0.9954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:41.988420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8 × 10-5
5-th percentile0.28066
Q10.35897
median0.43731
Q30.56682
95-th percentile0.91644
Maximum0.9954
Range0.99532
Interquartile range (IQR)0.20785

Descriptive statistics

Standard deviation0.18163371
Coefficient of variation (CV)0.37417471
Kurtosis0.56514099
Mean0.48542486
Median Absolute Deviation (MAD)0.09366
Skewness1.072518
Sum94459.795
Variance0.032990803
MonotonicityNot monotonic
2023-09-24T22:00:42.228078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.38249 9161
 
4.7%
0.44352 7145
 
3.7%
0.30859 6887
 
3.5%
0.32128 6164
 
3.2%
0.41471 5455
 
2.8%
0.39447 4635
 
2.4%
0.39849 4614
 
2.4%
0.46226 4601
 
2.4%
0.34365 4085
 
2.1%
0.5042 3877
 
2.0%
Other values (337) 137968
70.9%
ValueCountFrequency (%)
8 × 10-5179
0.1%
0.03786 32
 
< 0.1%
0.05742 3
 
< 0.1%
0.10579 9
 
< 0.1%
0.12278 1
 
< 0.1%
0.12831 108
0.1%
0.1302 11
 
< 0.1%
0.13116 1
 
< 0.1%
0.13602 1
 
< 0.1%
0.14103 6
 
< 0.1%
ValueCountFrequency (%)
0.9954 1
 
< 0.1%
0.99379 9
 
< 0.1%
0.99169 9
 
< 0.1%
0.9914 5
 
< 0.1%
0.99133 26
 
< 0.1%
0.99104 1
 
< 0.1%
0.99026 149
0.1%
0.98811 27
 
< 0.1%
0.9833 109
0.1%
0.9826 19
 
< 0.1%

cont10
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49804891
Minimum0
Maximum0.99498
Zeros232
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:42.466070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21983
Q10.36458
median0.46119
Q30.61459
95-th percentile0.81923
Maximum0.99498
Range0.99498
Interquartile range (IQR)0.25001

Descriptive statistics

Standard deviation0.18591451
Coefficient of variation (CV)0.37328565
Kurtosis-0.85207051
Mean0.49804891
Median Absolute Deviation (MAD)0.13184
Skewness0.355381
Sum96916.334
Variance0.034564206
MonotonicityNot monotonic
2023-09-24T22:00:42.704015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45017 7415
 
3.8%
0.43919 7355
 
3.8%
0.8351 6024
 
3.1%
0.47779 5074
 
2.6%
0.39599 5065
 
2.6%
0.50556 4933
 
2.5%
0.2123 4532
 
2.3%
0.44467 4468
 
2.3%
0.36458 4186
 
2.2%
0.37493 4099
 
2.1%
Other values (164) 141441
72.7%
ValueCountFrequency (%)
0 232
0.1%
0.06232 1
 
< 0.1%
0.07664 32
 
< 0.1%
0.09777 7
 
< 0.1%
0.10381 4
 
< 0.1%
0.11461 1
 
< 0.1%
0.12886 2
 
< 0.1%
0.13137 16
 
< 0.1%
0.13393 1
 
< 0.1%
0.13917 4
 
< 0.1%
ValueCountFrequency (%)
0.99498 3
 
< 0.1%
0.99475 10
 
< 0.1%
0.99463 47
< 0.1%
0.99451 40
< 0.1%
0.99048 46
< 0.1%
0.95643 2
 
< 0.1%
0.95454 9
 
< 0.1%
0.87114 54
< 0.1%
0.86607 2
 
< 0.1%
0.85815 5
 
< 0.1%

cont11
Real number (ℝ)

HIGH CORRELATION 

Distinct326
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4934313
Minimum0.035321
Maximum0.998742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:43.218827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.035321
5-th percentile0.199654
Q10.310961
median0.457203
Q30.678924
95-th percentile0.837272
Maximum0.998742
Range0.963421
Interquartile range (IQR)0.367963

Descriptive statistics

Standard deviation0.20973944
Coefficient of variation (CV)0.4250631
Kurtosis-1.0499594
Mean0.4934313
Median Absolute Deviation (MAD)0.165935
Skewness0.28111892
Sum96017.784
Variance0.043990634
MonotonicityNot monotonic
2023-09-24T22:00:43.464938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.341813 7523
 
3.9%
0.453334 6704
 
3.4%
0.4922 6190
 
3.2%
0.24541 6126
 
3.1%
0.644013 4475
 
2.3%
0.415029 4306
 
2.2%
0.307628 4155
 
2.1%
0.569745 4111
 
2.1%
0.6075 4071
 
2.1%
0.223038 3966
 
2.0%
Other values (316) 142965
73.5%
ValueCountFrequency (%)
0.035321 66
< 0.1%
0.068798 1
 
< 0.1%
0.07949 2
 
< 0.1%
0.089115 1
 
< 0.1%
0.09168 6
 
< 0.1%
0.098386 69
< 0.1%
0.102616 1
 
< 0.1%
0.107007 1
 
< 0.1%
0.111562 3
 
< 0.1%
0.116286 1
 
< 0.1%
ValueCountFrequency (%)
0.998742 2
 
< 0.1%
0.998702 1
 
< 0.1%
0.998309 1
 
< 0.1%
0.99783 1
 
< 0.1%
0.997796 4
 
< 0.1%
0.996799 3
 
< 0.1%
0.995831 3
 
< 0.1%
0.995423 2
 
< 0.1%
0.995352 10
< 0.1%
0.995279 2
 
< 0.1%

cont12
Real number (ℝ)

HIGH CORRELATION 

Distinct328
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4930718
Minimum0.036232
Maximum0.998484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:43.686291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.036232
5-th percentile0.204687
Q10.308395
median0.462286
Q30.675759
95-th percentile0.843026
Maximum0.998484
Range0.962252
Interquartile range (IQR)0.367364

Descriptive statistics

Standard deviation0.20944157
Coefficient of variation (CV)0.42476891
Kurtosis-1.0281223
Mean0.4930718
Median Absolute Deviation (MAD)0.168567
Skewness0.29256875
Sum95947.828
Variance0.043865773
MonotonicityNot monotonic
2023-09-24T22:00:43.945051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.481306 7245
 
3.7%
0.443374 7107
 
3.7%
0.241676 6260
 
3.2%
0.352251 6190
 
3.2%
0.335036 4814
 
2.5%
0.301921 4796
 
2.5%
0.594646 4140
 
2.1%
0.714544 3850
 
2.0%
0.785706 3825
 
2.0%
0.630853 3790
 
1.9%
Other values (318) 142575
73.3%
ValueCountFrequency (%)
0.036232 66
< 0.1%
0.075698 1
 
< 0.1%
0.080083 2
 
< 0.1%
0.089555 1
 
< 0.1%
0.09466 6
 
< 0.1%
0.098659 69
< 0.1%
0.102807 1
 
< 0.1%
0.10711 1
 
< 0.1%
0.111569 3
 
< 0.1%
0.119363 1
 
< 0.1%
ValueCountFrequency (%)
0.998484 2
 
< 0.1%
0.998437 1
 
< 0.1%
0.997975 1
 
< 0.1%
0.997416 1
 
< 0.1%
0.997376 4
 
< 0.1%
0.997211 2
 
< 0.1%
0.996219 3
 
< 0.1%
0.995104 3
 
< 0.1%
0.994637 2
 
< 0.1%
0.994555 10
< 0.1%

cont13
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49311225
Minimum0.000228
Maximum0.988494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:44.190727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.000228
5-th percentile0.250991
Q10.315758
median0.363547
Q30.689974
95-th percentile0.851532
Maximum0.988494
Range0.988266
Interquartile range (IQR)0.374216

Descriptive statistics

Standard deviation0.21278512
Coefficient of variation (CV)0.43151457
Kurtosis-1.3493265
Mean0.49311225
Median Absolute Deviation (MAD)0.117536
Skewness0.38060095
Sum95955.699
Variance0.045277508
MonotonicityNot monotonic
2023-09-24T22:00:44.463419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.339244 9192
 
4.7%
0.689974 5435
 
2.8%
0.30435 4934
 
2.5%
0.315758 4717
 
2.4%
0.333292 4714
 
2.4%
0.318646 3944
 
2.0%
0.345247 3902
 
2.0%
0.298734 3725
 
1.9%
0.611431 3513
 
1.8%
0.605077 3133
 
1.6%
Other values (343) 147383
75.7%
ValueCountFrequency (%)
0.000228 232
0.1%
0.073554 3
 
< 0.1%
0.074468 3
 
< 0.1%
0.078228 3
 
< 0.1%
0.081162 3
 
< 0.1%
0.090572 1
 
< 0.1%
0.093921 1
 
< 0.1%
0.096216 2
 
< 0.1%
0.09856 18
 
< 0.1%
0.09975 7
 
< 0.1%
ValueCountFrequency (%)
0.988494 5
 
< 0.1%
0.985419 1
 
< 0.1%
0.982479 3
 
< 0.1%
0.982248 2
 
< 0.1%
0.948826 1
 
< 0.1%
0.9441 7
 
< 0.1%
0.942676 4
 
< 0.1%
0.941218 6
 
< 0.1%
0.938965 74
< 0.1%
0.936632 5
 
< 0.1%

cont14
Real number (ℝ)

Distinct18740
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49559426
Minimum0.179722
Maximum0.844848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:44.686028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.179722
5-th percentile0.214612
Q10.29461
median0.407325
Q30.72461
95-th percentile0.823247
Maximum0.844848
Range0.665126
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.22248627
Coefficient of variation (CV)0.44892826
Kurtosis-1.5294308
Mean0.49559426
Median Absolute Deviation (MAD)0.176339
Skewness0.24983041
Sum96438.678
Variance0.049500139
MonotonicityNot monotonic
2023-09-24T22:00:44.930229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.297706 185
 
0.1%
0.740079 178
 
0.1%
0.297137 161
 
0.1%
0.22944 156
 
0.1%
0.229429 155
 
0.1%
0.295676 142
 
0.1%
0.233142 141
 
0.1%
0.237754 132
 
0.1%
0.297815 129
 
0.1%
0.724461 127
 
0.1%
Other values (18730) 193086
99.2%
ValueCountFrequency (%)
0.179722 1
< 0.1%
0.180268 1
< 0.1%
0.180421 1
< 0.1%
0.18045 1
< 0.1%
0.180479 1
< 0.1%
0.180512 1
< 0.1%
0.180522 1
< 0.1%
0.180546 1
< 0.1%
0.180584 2
< 0.1%
0.180608 1
< 0.1%
ValueCountFrequency (%)
0.844848 1
 
< 0.1%
0.844844 2
 
< 0.1%
0.844831 1
 
< 0.1%
0.844729 12
< 0.1%
0.84447 1
 
< 0.1%
0.844448 2
 
< 0.1%
0.844402 1
 
< 0.1%
0.844312 4
 
< 0.1%
0.844308 2
 
< 0.1%
0.844303 3
 
< 0.1%

loss
Real number (ℝ)

Distinct158223
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3036.2511
Minimum0.67
Maximum121012.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-09-24T22:00:45.158523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.67
5-th percentile616.8
Q11204.2625
median2114.585
Q33862.4925
95-th percentile8510.357
Maximum121012.25
Range121011.58
Interquartile range (IQR)2658.23

Descriptive statistics

Standard deviation2900.9777
Coefficient of variation (CV)0.95544723
Kurtosis47.041485
Mean3036.2511
Median Absolute Deviation (MAD)1111.09
Skewness3.7616691
Sum5.9083017 × 108
Variance8415671.5
MonotonicityNot monotonic
2023-09-24T22:00:45.402607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
580 104
 
0.1%
1080 27
 
< 0.1%
330 18
 
< 0.1%
280 13
 
< 0.1%
700 13
 
< 0.1%
800 11
 
< 0.1%
300 10
 
< 0.1%
480 9
 
< 0.1%
80 9
 
< 0.1%
750 9
 
< 0.1%
Other values (158213) 194369
99.9%
ValueCountFrequency (%)
0.67 1
< 0.1%
5.25 1
< 0.1%
6 1
< 0.1%
8.4 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
20.11 1
< 0.1%
20.99 1
< 0.1%
21 1
< 0.1%
23.69 1
< 0.1%
ValueCountFrequency (%)
121012.25 1
< 0.1%
106863 1
< 0.1%
85923.56 1
< 0.1%
79623.52 1
< 0.1%
67667.16 1
< 0.1%
67537.73 1
< 0.1%
59826.79 1
< 0.1%
57224.55 1
< 0.1%
54190.39 1
< 0.1%
51383.04 1
< 0.1%

Unnamed: 132
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing188318
Missing (%)96.8%
Memory size1.5 MiB
6274 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6274
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
6274
 
3.2%
(Missing) 188318
96.8%

Length

2023-09-24T22:00:45.608060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-24T22:00:45.786593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
6274
100.0%

Most occurring categories

ValueCountFrequency (%)
Control 6274
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
6274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6274
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6274
100.0%

Interactions

2023-09-24T21:59:40.583894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:57.889228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:00.818695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:03.599006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:06.143205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:09.100862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:11.915487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:14.759447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:17.709823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:20.440781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:23.365535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:26.324006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:29.105588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:31.895956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:34.921775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:37.716645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:40.761684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:58.071855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:00.990458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:03.767448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:06.304007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:09.270512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:12.090774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:14.934250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:17.880174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:20.632579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:23.544781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:26.497000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:29.280703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:32.072233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:35.104225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:37.907353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:40.930986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:58.236588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:01.175778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:03.925505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:06.498510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:09.434882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:12.268882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:15.101576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:18.042855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:20.805657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:23.707657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:26.672427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:29.445237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:32.246319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:35.274967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:38.096586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:41.102365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:58.396130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:01.360497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:04.082742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:06.668896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:09.597114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:12.486838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:15.264936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:18.206236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:20.996611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:23.883154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:26.838192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:29.621979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:32.406660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:35.445536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:38.260971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:41.272466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:58.556221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:01.535436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:04.242092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:06.845140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:09.752615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:12.657215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:15.431054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:18.365647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:21.192025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:24.042565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:27.005890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:29.785677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:32.573821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:35.615190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:38.465202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:41.456053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:58.884311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:01.697786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:04.401321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:07.044874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:09.926685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:12.834735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:15.601523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:18.550126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:21.383120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:24.452819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:27.190739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:29.980147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:32.753647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:35.790222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:38.649285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:41.632483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:59.060388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:01.876732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:04.563751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:07.455918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:10.098875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:13.010283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:16.005059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:18.727253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:21.579089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:24.627454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:27.373432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:30.159208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:32.954238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:35.965007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:38.827622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:41.803996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:59.232245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:02.052476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:04.719951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:07.623609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:10.276725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:13.185078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:16.171503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:18.897449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:21.767104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:24.789178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:27.556394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:30.333037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:33.122345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:36.134294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:39.000624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:41.973178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:59.424900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:02.223923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:04.872563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:07.779123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:10.451341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:13.350601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:16.332304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:19.058649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:21.941100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:24.952591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:27.720359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:30.496837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:33.288192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:36.298180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:39.169033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:42.419732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:59.634423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:02.420400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:05.044969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:07.954829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:10.624815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:13.539668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:16.521643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:19.239345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:22.121731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:25.123619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:27.904558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:30.685074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:33.730086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:36.480433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:39.352161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:42.602183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:59.798861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:02.599329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:05.198732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:08.108734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:10.788233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:13.708749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:16.681805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:19.402813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:22.301436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:25.294536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:28.072062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:30.854261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:33.899581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:36.657329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:39.524208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:42.775290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:58:59.962431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:02.766539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:05.354069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:08.262046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:10.965865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:13.880873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:16.858481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:19.574216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:22.480885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:25.458448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:28.254427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:31.021841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:34.066051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:36.827776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:39.706142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:42.955127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:00.147881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:02.953175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:05.515839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:08.422044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:11.148434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:14.051682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:17.034191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:19.736714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:22.660428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:25.635788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:28.420960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:31.198192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:34.234376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:36.997728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:39.878476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:43.126409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:00.308215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:03.122275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:05.669484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:08.591783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:11.334859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:14.224908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:17.204349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:19.913313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:22.839586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:25.808509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:28.593724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:31.364392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:34.407146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:37.170641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:40.054132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:43.310436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:00.464952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:03.279446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:05.826897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:08.769769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:11.510888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:14.401643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:17.367560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:20.080107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:23.012164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:25.972814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:28.758751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:31.542136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:34.571660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:37.343577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:40.222557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:43.494201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:00.648037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:03.439851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:05.984519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:08.931836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:11.687310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:14.584033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:17.541883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:20.258608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:23.186471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:26.148972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:28.932503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:31.712597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:34.746048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:37.525869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-24T21:59:40.405689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-24T22:00:46.240046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idcont1cont2cont3cont4cont5cont6cont7cont8cont9cont10cont11cont12cont13cont14losscat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cat19cat20cat21cat22cat23cat24cat25cat26cat27cat28cat29cat30cat31cat32cat33cat34cat35cat36cat37cat38cat39cat40cat41cat42cat43cat44cat45cat46cat47cat48cat49cat50cat51cat52cat53cat54cat55cat56cat57cat58cat59cat60cat61cat62cat63cat64cat65cat66cat67cat68cat69cat70cat71cat72cat73cat74cat75cat76cat77cat78cat79cat80cat81cat82cat83cat84cat85cat86cat87cat88cat89cat90cat91cat92cat93cat94cat95cat96cat97cat98cat99cat100cat101cat102cat103cat104cat105cat106cat107cat108cat111cat114cat115
id1.0000.002-0.0000.0010.002-0.0000.0000.0010.0040.0010.0010.0000.0000.000-0.005-0.0010.0040.0040.0000.0020.0000.0050.0050.0000.0040.0030.0000.0000.0050.0000.0030.0030.0050.0050.0000.0030.0000.0060.0000.0000.0000.0060.0040.0040.0050.0080.0000.0030.0000.0000.0000.0000.0030.0000.0000.0030.0050.0000.0070.0000.0000.0000.0000.0040.0050.0050.0000.0000.0000.0020.0000.0000.0010.0050.0000.0020.0000.0060.0070.0000.0000.0050.0070.0050.0040.0020.0050.0000.0000.0000.0040.0000.0000.0020.0000.0030.0040.0000.0000.0000.0000.0030.0040.0000.0000.0000.0000.0000.0010.0030.0000.0020.0000.0000.0030.0020.0040.0000.0030.0000.0030.0000.0000.0030.1580.0000.002
cont10.0021.000-0.076-0.3830.361-0.0060.7130.4590.2890.9060.7830.6080.6200.4630.042-0.0170.0460.0640.0430.0160.0500.0340.0200.0350.0590.0260.0280.0590.0240.0190.0260.0420.0180.0140.0150.0000.0050.0020.0350.0270.0150.0180.0370.0210.0110.0070.0030.1870.0080.0000.0810.0210.0730.0170.0080.0250.0250.2280.0130.0470.0150.0110.0100.0940.0000.0160.0170.0180.0450.0160.0690.0750.0250.0070.0070.0040.0100.0000.0090.0080.0140.0370.0150.0080.0030.0270.2290.0480.0120.0000.0710.0150.0020.0050.0300.0430.0160.0200.0170.0330.0040.4210.0290.4330.0070.0180.0340.0350.0060.1430.3050.1970.5330.4890.3190.0400.0230.0130.0180.3860.1630.3740.3670.4030.0070.0140.273
cont2-0.000-0.0761.0000.4640.0340.1770.0110.0460.118-0.0110.0780.0910.0820.087-0.0580.0800.0300.0460.0250.0120.0140.0390.0490.0150.0370.0240.0350.0390.0310.0140.0080.0250.0100.0130.0060.0070.0060.0000.0210.0180.0150.0130.0080.0150.0100.0140.0140.0220.0090.0070.0070.0100.0210.0050.0100.0160.0160.0260.0210.0230.0190.0070.0020.0010.0080.0440.0040.0090.0090.0160.0030.0110.0660.0050.0050.0000.0080.0000.0050.0000.0100.0130.0080.0030.0050.0020.0770.0270.0120.0000.3330.0140.0050.0030.0310.0280.0130.0260.0630.0210.0050.0570.0200.0460.0180.0100.0130.0070.0170.3330.0440.0740.0640.0680.0670.0170.0380.0150.0070.1170.0760.0800.1170.0610.0060.0340.105
cont30.001-0.3830.4641.000-0.3480.079-0.3090.053-0.189-0.325-0.2850.012-0.001-0.367-0.0490.0680.0260.0360.0250.0090.0180.0310.0340.0240.0320.0220.0220.0390.0240.0220.0200.0270.0100.0120.0200.0000.0050.0030.0140.0200.0070.0210.0200.0110.0130.0180.0100.1130.0120.0000.0500.0120.0310.0120.0100.0200.0220.1320.0180.0140.0120.0130.0090.0480.0150.0400.0220.0130.0220.0100.0360.0480.0460.0080.0060.0040.0150.0040.0000.0000.0100.0290.0100.0000.0040.0170.6610.0440.0100.0000.1740.0250.0070.0080.0200.0270.0190.0170.0360.0290.0060.2450.0180.2510.0130.0100.0220.0220.0080.2940.1730.2010.2130.2790.1630.0270.0200.0110.0070.3520.2020.1760.1630.2270.0080.0180.219
cont40.0020.3610.034-0.3481.0000.1890.205-0.0630.5460.3270.2830.1050.1140.2090.024-0.0270.0210.0380.0260.0190.0270.0380.0300.0200.0350.0240.0270.0450.0240.0080.0130.0220.0100.0110.0180.0000.0040.0080.0190.0190.0110.0080.0180.0170.0090.0070.0130.0830.0060.0000.0380.0110.0340.0180.0120.0220.0190.1000.0140.0200.0160.0110.0100.0420.0090.0360.0180.0120.0310.0110.0280.0300.0370.0000.0070.0080.0030.0040.0030.0050.0090.0240.0070.0000.0040.0110.0880.0230.0100.0070.0740.0120.0020.0030.0240.0290.0170.0140.0130.0300.0030.2280.0170.1920.0110.0100.0240.0260.0080.1710.1680.1740.2210.2450.1630.0290.0180.0080.0110.2730.1050.1810.1660.2370.0080.0150.182
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cont60.0000.7130.011-0.3090.205-0.1571.0000.7750.4090.8000.8710.8060.8130.7330.0280.0200.0480.0720.0290.0230.0360.0570.0120.0300.0630.0330.0470.0910.0410.0150.0160.0310.0150.0150.0150.0000.0050.0090.0370.0200.0220.0080.0270.0170.0100.0080.0290.1210.0110.0080.0600.0200.0620.0150.0150.0240.0240.1470.0090.0430.0080.0050.0110.0690.0060.0330.0330.0190.0460.0230.0460.0500.0200.0110.0060.0050.0100.0000.0000.0070.0140.0340.0170.0000.0000.0160.2870.0550.0150.0030.0380.0180.0040.0040.0290.0440.0190.0230.0270.0290.0050.4950.0260.2740.0060.0120.0330.0330.0070.1440.3300.2290.5850.4570.3030.0410.0280.0110.0130.5020.2130.5440.3250.4420.0110.0220.359
cont70.0010.4590.0460.053-0.063-0.2370.7751.0000.2310.5600.6070.8040.8020.3360.0140.0550.0520.0610.0250.0240.0170.0510.0180.0240.0530.0300.0430.0770.0390.0080.0030.0340.0120.0090.0200.0040.0020.0080.0360.0200.0240.0090.0160.0170.0110.0160.0230.0500.0000.0000.0270.0210.0400.0120.0120.0230.0230.0590.0190.0310.0150.0030.0100.0310.0090.0410.0170.0160.0280.0170.0210.0190.0160.0100.0080.0100.0060.0000.0000.0050.0100.0260.0120.0060.0020.0090.1690.0700.0140.0000.0510.0060.0000.0000.0220.0370.0280.0250.0330.0220.0040.3260.0220.1250.0080.0100.0230.0220.0120.1320.2280.0920.4650.2920.2170.0330.0240.0100.0070.3130.2340.4050.2600.4330.0090.0190.262
cont80.0040.2890.118-0.1890.5460.0320.4090.2311.0000.3750.2910.2920.3020.4430.0340.0280.0270.0460.0180.0110.0280.0400.0160.0180.0400.0200.0290.0490.0260.0090.0190.0180.0120.0090.0030.0090.0090.0020.0200.0060.0130.0080.0180.0060.0050.0050.0120.0960.0080.0000.0400.0140.0400.0130.0110.0140.0110.1170.0100.0250.0080.0020.0110.0590.0030.0280.0210.0140.0390.0150.0320.0370.0200.0050.0020.0010.0040.0030.0000.0000.0090.0210.0090.0000.0000.0130.1170.0230.0080.0030.0600.0130.0060.0040.0080.0200.0070.0150.0180.0220.0050.3030.0120.2290.0080.0100.0230.0250.0050.1680.2420.1190.1650.2920.2080.0270.0180.0070.0120.2210.0990.1780.1950.2200.0000.0160.227
cont90.0010.906-0.011-0.3250.327-0.0960.8000.5600.3751.0000.7940.6740.6840.5520.0480.0050.0440.0680.0390.0210.0490.0400.0150.0260.0600.0270.0340.0700.0320.0160.0280.0390.0160.0150.0150.0000.0090.0090.0360.0220.0140.0180.0370.0170.0090.0040.0140.1930.0110.0020.0880.0230.0730.0130.0100.0190.0210.2310.0140.0480.0130.0090.0100.1010.0050.0250.0260.0170.0500.0160.0700.0770.0210.0030.0070.0050.0080.0020.0060.0110.0130.0300.0150.0050.0010.0280.2000.0480.0130.0030.0600.0150.0030.0100.0260.0400.0160.0210.0220.0290.0030.5140.0280.4570.0060.0160.0340.0340.0100.1610.3480.1760.5880.5460.3380.0400.0250.0090.0180.3780.1680.3740.3650.4800.0110.0170.299
cont100.0010.7830.078-0.2850.283-0.0650.8710.6070.2910.7941.0000.7360.7430.6420.0310.0030.0500.0780.0360.0180.0470.0510.0160.0330.0670.0400.0480.0830.0420.0170.0150.0400.0190.0170.0160.0000.0050.0070.0380.0220.0160.0100.0310.0210.0090.0100.0160.1350.0100.0080.0670.0180.0720.0220.0050.0280.0240.1670.0150.0540.0090.0090.0130.0770.0080.0350.0210.0160.0460.0200.0490.0520.0220.0080.0070.0020.0080.0000.0080.0080.0180.0350.0150.0000.0000.0170.1600.0480.0120.0040.0830.0170.0050.0020.0280.0460.0170.0200.0290.0310.0000.4730.0290.3000.0070.0150.0320.0320.0070.1480.3150.1780.6000.4670.3010.0400.0290.0120.0170.4070.2210.4350.3530.4510.0070.0200.351
cont110.0000.6080.0910.0120.105-0.1740.8060.8040.2920.6740.7361.0000.9950.4370.0370.0550.0500.0640.0260.0230.0280.0510.0160.0120.0530.0260.0420.0740.0370.0090.0110.0340.0120.0100.0170.0000.0000.0000.0350.0140.0220.0120.0290.0110.0090.0130.0180.0750.0090.0000.0350.0190.0540.0070.0120.0160.0180.0910.0240.0310.0180.0040.0060.0440.0000.0390.0120.0140.0250.0190.0270.0290.0230.0020.0050.0070.0090.0020.0050.0000.0070.0140.0140.0020.0000.0110.1490.0660.0110.0090.0820.0080.0050.0040.0180.0320.0240.0240.0330.0260.0030.2860.0220.1710.0060.0100.0310.0310.0080.1320.1820.1420.3650.2660.1980.0390.0240.0050.0100.4760.3480.4110.2130.2500.0100.0190.237
cont120.0000.6200.082-0.0010.114-0.1670.8130.8020.3020.6840.7430.9951.0000.4470.0400.0540.0490.0640.0260.0240.0300.0510.0160.0090.0540.0270.0400.0740.0350.0100.0110.0350.0110.0100.0190.0000.0020.0000.0350.0120.0210.0120.0290.0080.0110.0120.0180.0840.0100.0000.0370.0200.0540.0060.0070.0150.0170.1030.0240.0300.0200.0050.0070.0490.0070.0400.0150.0130.0240.0200.0300.0360.0230.0060.0020.0060.0080.0010.0000.0010.0080.0110.0160.0000.0000.0130.1600.0660.0110.0080.0800.0100.0050.0050.0190.0320.0240.0240.0340.0260.0020.2860.0220.1890.0070.0100.0320.0320.0080.1370.1830.1660.3790.2700.2080.0400.0250.0040.0110.4840.3510.4180.2100.2530.0090.0190.234
cont130.0000.4630.087-0.3670.209-0.0190.7330.3360.4430.5520.6420.4370.4471.0000.0360.0070.0260.0600.0240.0270.0390.0660.0080.0350.0490.0310.0480.0770.0390.0170.0190.0240.0110.0060.0220.0030.0030.0090.0330.0240.0190.0340.0310.0190.0180.0160.0350.1420.0120.0110.0730.0140.0600.0130.0250.0260.0280.1710.0150.0500.0170.0050.0130.0750.0160.0410.0510.0270.0610.0230.0530.0570.0130.0070.0040.0080.0120.0050.0000.0070.0120.0400.0210.0060.0120.0170.2470.0390.0160.0100.0960.0230.0050.0070.0250.0370.0170.0110.0170.0270.0060.7300.0220.3180.0060.0100.0290.0290.0080.1750.5060.2090.4710.6080.4930.0370.0240.0120.0150.3620.2220.3320.3870.5860.0120.0250.512
cont14-0.0050.042-0.058-0.0490.024-0.0360.0280.0140.0340.0480.0310.0370.0400.0361.0000.0010.0410.0420.0290.0260.0280.0520.0160.0380.0410.0420.0300.0350.0290.0210.0000.0310.0150.0140.0100.0050.0070.0000.0380.0190.0260.0290.0120.0220.0080.0000.0230.0270.0040.0040.0080.0350.0050.0250.0170.0340.0250.0290.0070.0230.0190.0000.0060.0100.0110.0570.0060.0090.0190.0130.0130.0130.0170.0000.0050.0020.0090.0050.0000.0000.0120.0350.0120.0070.0120.0050.0380.1790.0560.0060.0580.0130.0060.0070.0330.0410.0230.0370.0340.0810.0080.0560.0270.0570.0060.0120.0630.0540.0110.0450.0480.0440.0500.0760.0380.0620.0180.0150.0130.0560.0260.0330.0380.0610.0110.0200.041
loss-0.001-0.0170.0800.068-0.027-0.0150.0200.0550.0280.0050.0030.0550.0540.0070.0011.0000.0650.0710.0730.0400.0410.0250.2170.0320.0710.1030.0850.1000.0860.0630.0000.0700.0470.0160.0120.0070.0000.0000.0580.0380.0300.0120.0000.0500.0360.0180.0120.0050.0270.0130.0100.0590.0000.0320.0050.0520.0320.0090.0190.0110.0300.0260.0220.0000.0080.0250.0170.0120.0130.0090.0000.0000.2580.0230.0260.0000.0100.0130.0030.0210.0310.0200.0210.0140.0000.0000.0520.0890.0350.0110.0340.0440.0000.0200.1290.1070.0660.0230.0130.0140.0340.0350.0980.0000.0830.0310.0250.0080.0020.0250.0200.0080.0160.0100.0350.0360.0490.0140.0220.0250.1850.0560.0570.0350.0220.0230.161
cat10.0040.0460.0300.0260.0210.0180.0480.0520.0270.0440.0500.0500.0490.0260.0410.0651.0000.1690.0980.0270.0120.0790.0700.0840.1600.1420.1020.1450.0980.0410.0000.0780.0350.0290.0410.0130.0160.0010.0790.0550.0270.0040.0400.0690.0490.0240.0310.0000.0150.0070.0010.0620.0640.0000.0160.0850.0660.0110.0190.0520.0470.0130.0000.0060.0130.0710.0100.0080.0220.0110.0080.0020.0610.0150.0190.0140.0240.0060.0080.0060.0380.0770.0200.0090.0030.0000.0000.2030.3030.0070.0150.0410.0060.0260.1710.2130.0970.1020.0270.0720.0360.0230.1430.0050.0690.0980.1300.0200.0340.0320.0100.0080.0480.0220.0310.5170.1890.0840.0600.0420.0340.0510.0320.0360.0670.0990.031
cat20.0040.0640.0460.0360.0380.0200.0720.0610.0460.0680.0780.0640.0640.0600.0420.0710.1691.0000.1390.0340.0630.4380.0700.0000.9320.4790.3950.4830.3880.1260.0150.1930.0450.0060.0470.0240.0230.0070.2380.0430.0800.1560.2330.0580.0830.0870.0530.0440.0250.0310.0130.2320.2460.0890.0390.0420.0270.0560.0950.1880.0850.0170.0330.0160.1560.4080.0550.1490.1970.1070.0160.0160.0770.0130.0000.0060.0140.0000.0000.0000.0070.0000.0110.0000.0170.0080.0120.1370.1330.0050.0290.0550.0520.0560.1760.2190.0570.0850.0120.0580.0580.0580.2310.0340.0710.1390.1200.0420.0290.0280.0380.0280.0620.0620.0600.4771.0000.0100.1130.0600.0340.0600.0460.0680.0770.4380.051
cat30.0000.0430.0250.0250.0260.0220.0290.0250.0180.0390.0360.0260.0260.0240.0290.0730.0980.1391.0000.0170.0220.0500.0830.1230.1410.2150.1180.1990.1230.0280.0060.7830.3460.3010.4090.1380.1950.0610.0360.0000.0320.0440.0500.0040.0060.0130.0280.0100.0070.0080.0030.0320.0520.0350.0270.0020.0030.0110.0170.0580.0040.0060.0080.0050.0140.0410.0070.0090.0040.0030.0050.0040.0740.0190.0150.0290.0220.0090.0110.0100.0480.1240.0000.0000.0000.0000.0120.0780.0760.0060.0150.0150.0140.0210.2390.3010.0340.0370.0200.0660.0310.0060.5080.0170.0841.0000.1000.0130.0170.0090.0090.0130.0390.0200.0250.1880.2330.1250.0430.0290.0180.0340.0310.0330.0350.0600.017
cat40.0020.0160.0120.0090.0190.0040.0230.0240.0110.0210.0180.0230.0240.0270.0260.0400.0270.0340.0171.0000.2790.1310.0320.0030.0330.0180.1930.0020.0940.0130.0000.0040.0000.0040.0260.0100.0000.0070.6480.2730.4810.3690.5060.2950.2080.2040.2500.1170.1050.0820.0490.1420.1850.1750.0870.0680.0800.0420.0670.1740.0530.0350.0290.0190.0830.1290.0210.0960.0720.0330.0030.0020.0270.0060.0050.0060.0190.0020.0000.0000.0180.0060.0000.0000.0220.0000.0050.0680.0460.0080.0080.0540.0410.0460.0850.1270.1520.0290.0290.0740.0450.0240.0540.0140.0320.0190.0740.0420.0110.0160.0140.0060.0170.0320.0250.2090.0740.0050.2850.0240.0040.0200.0220.0341.0000.1340.017
cat50.0000.0500.0140.0180.0270.0020.0360.0170.0280.0490.0470.0280.0300.0390.0280.0410.0120.0630.0220.2791.0000.1670.0260.0140.0700.0520.1090.0210.1780.0220.0000.0080.0020.0110.0160.0000.0150.0020.1500.0780.1770.1490.1800.0680.0500.0660.0940.0320.0390.0290.0150.6260.5110.4640.2270.2960.2750.1320.2090.4160.2120.0950.0850.0530.1130.1630.0270.0640.0850.0460.0060.0020.0270.0040.0000.0030.0140.0000.0020.0000.0190.0080.0000.0050.0060.0050.0070.0190.0070.0340.0000.0560.0430.0480.0680.0850.1270.0220.0300.0630.0470.0210.0480.0410.0270.0240.1110.0400.0100.0110.0240.0220.0290.0470.0240.2010.0840.0141.0000.0400.0140.0250.0280.0490.2830.1690.024
cat60.0050.0340.0390.0310.0380.0180.0570.0510.0400.0400.0510.0510.0510.0660.0520.0250.0790.4380.0500.1310.1671.0000.0490.0580.4090.2270.1930.2310.1880.0450.0070.1060.0350.0250.1100.0080.0020.0010.2360.0990.1550.0580.1650.1080.0240.0280.0750.0210.0300.0010.0070.2410.1180.1590.0730.1130.1070.0100.0040.0920.0050.0320.0070.0120.3450.9260.1240.3380.4550.2400.0420.0480.0570.0000.0130.0030.0220.0000.0040.0000.0060.0640.0050.0070.0140.0090.0010.0220.0570.0320.0150.0410.0400.0430.1350.1700.1330.0170.0090.0310.0470.0620.1520.0230.0490.0520.1360.0870.0190.0410.0430.0250.0280.0620.0640.5250.4390.0580.1800.0450.0180.0380.0540.0660.1491.0000.059
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cat960.0020.1970.0740.2010.1740.0730.2290.0920.1190.1760.1780.1420.1660.2090.0440.0080.0080.0280.0130.0060.0220.0250.0060.0140.0260.0150.0170.0170.0150.0100.0160.0110.0000.0130.0070.0070.0050.0000.0150.0020.0060.0040.0200.0060.0000.0000.0110.0750.0070.0100.0410.0100.0350.0050.0080.0060.0070.0990.0070.0090.0010.0000.0070.0420.0070.0200.0140.0090.0270.0140.0410.0400.0100.0040.0000.0000.0090.0000.0000.0050.0100.0130.0100.0000.0040.0080.5390.0280.0090.0000.0440.0160.0100.0070.0080.0160.0100.0110.0060.0180.0070.1140.0110.1570.0020.0070.0220.0220.0020.0970.0811.0000.1660.1690.1710.0260.0130.0070.0090.3990.1640.2140.0880.1360.0030.0130.174
cat970.0000.5330.0640.2130.2210.1260.5850.4650.1650.5880.6000.3650.3790.4710.0500.0160.0480.0620.0390.0170.0290.0280.0160.0220.0590.0300.0290.0620.0230.0140.0120.0420.0170.0140.0170.0000.0040.0090.0350.0200.0130.0130.0180.0170.0070.0000.0050.0890.0000.0030.0390.0210.0490.0110.0030.0190.0170.1070.0090.0460.0080.0090.0130.0430.0000.0180.0270.0040.0350.0120.0310.0350.0220.0030.0080.0100.0070.0000.0000.0100.0120.0220.0110.0000.0000.0100.1450.0490.0140.0060.0690.0170.0000.0040.0280.0400.0170.0220.0160.0280.0050.4070.0270.2050.0070.0160.0290.0270.0100.0860.2580.1661.0000.3950.3390.0420.0270.0090.0120.5740.2710.6820.5760.5470.0050.0140.360
cat980.0000.4890.0680.2790.2450.1420.4570.2920.2920.5460.4670.2660.2700.6080.0760.0100.0220.0620.0200.0320.0470.0620.0100.0340.0530.0330.0420.0730.0350.0160.0370.0150.0130.0060.0290.0000.0010.0000.0310.0250.0170.0190.0350.0190.0160.0130.0270.2230.0110.0000.0920.0180.0680.0130.0210.0270.0300.2660.0110.0440.0140.0050.0130.1040.0150.0410.0550.0260.0670.0220.0770.0870.0140.0000.0050.0010.0070.0060.0000.0000.0140.0400.0200.0000.0070.0320.1280.0290.0110.0090.0650.0210.0070.0100.0250.0380.0180.0080.0230.0310.0070.8150.0210.5360.0030.0110.0400.0380.0000.2190.5610.1690.3951.0000.7060.0540.0360.0190.0250.3900.1650.2460.5580.9940.0170.0370.545
cat990.0030.3190.0670.1630.1630.1080.3030.2170.2080.3380.3010.1980.2080.4930.0380.0350.0310.0600.0250.0250.0240.0640.0190.0430.0520.0310.0450.0760.0390.0090.0140.0250.0160.0100.0230.0000.0000.0000.0310.0300.0200.0570.0160.0250.0210.0190.0250.1010.0080.0000.0420.0200.0430.0150.0220.0340.0350.1200.0190.0670.0250.0060.0130.0470.0180.0370.0520.0260.0630.0210.0340.0390.0250.0000.0080.0080.0070.0000.0070.0030.0130.0470.0110.0000.0000.0120.1210.0280.0160.0130.0490.0220.0070.0070.0280.0400.0200.0080.0210.0260.0051.0000.0210.2460.0050.0090.0200.0180.0080.1360.9990.1710.3390.7061.0000.0230.0180.0150.0060.2030.2000.1690.2840.6350.0070.0180.375
cat1000.0020.0400.0170.0270.0290.0150.0410.0330.0270.0400.0400.0390.0400.0370.0620.0360.5170.4770.1880.2090.2010.5250.1140.2500.4640.4060.2610.4090.2440.1430.0090.2100.0610.0630.1660.0250.0400.0000.2110.1280.1860.1610.1640.1470.0760.0680.1440.0430.0480.0300.0230.1860.1400.1690.1080.1710.1470.0570.0840.1490.1020.0430.0300.0320.1770.5350.0670.1720.3320.1390.0270.0250.1230.0220.0340.0200.0460.0050.0140.0290.0730.2490.0700.0230.0200.0040.0420.4550.8620.7100.0250.0900.0330.0430.1470.1670.1130.1100.0230.0560.0470.0460.1480.0420.0430.0770.5660.5190.0340.0200.0330.0260.0420.0540.0231.0000.1430.0890.0710.0330.0180.0320.0220.0400.0680.1460.023
cat1010.0040.0230.0380.0200.0180.0120.0280.0240.0180.0250.0290.0240.0250.0240.0180.0490.1891.0000.2330.0740.0840.4390.1450.0360.9350.7980.6880.8050.6680.3820.1970.2950.0930.0510.0530.0790.0530.1540.2560.0490.0830.1570.2340.0620.0830.0880.0580.0440.0270.0310.0110.2450.2470.0930.0460.0500.0380.0560.0950.1890.0860.0280.0330.0140.1560.4090.0550.1490.1980.1070.0140.0140.1670.0940.0580.0480.0600.1490.1110.2070.0180.0460.0420.0180.0200.0000.0150.1900.1040.0130.0280.1590.0300.0350.1540.1830.0660.0790.0520.0390.0370.0390.1980.0210.2810.3090.0600.0310.0530.0220.0240.0130.0270.0360.0180.1431.0000.0670.0400.0210.0080.0160.0140.0250.1550.1060.015
cat1020.0000.0130.0150.0110.0080.0040.0110.0100.0070.0090.0120.0050.0040.0120.0150.0140.0840.0100.1250.0050.0140.0580.0611.0000.0080.0110.0220.0220.0220.0540.0030.0500.0890.1170.0870.0730.0720.1530.0080.0770.0150.0200.0280.1070.0780.0460.0210.0070.0430.0630.0060.0030.0330.0120.0160.1240.0900.0070.0500.0250.0860.0400.0390.0120.0200.0570.0310.0200.0090.0210.0110.0000.0310.0930.0830.0640.0840.2110.1570.2110.6110.8640.2540.1860.1860.0500.0220.0270.0600.0320.0050.0370.0060.0340.0870.1260.0170.0260.0200.0240.0220.0210.0820.0050.4640.3000.0840.0740.0340.0100.0100.0070.0090.0190.0150.0890.0671.0000.0870.0090.0090.0050.0100.0160.1270.0730.012
cat1030.0030.0180.0070.0070.0110.0030.0130.0070.0120.0180.0170.0100.0110.0150.0130.0220.0600.1130.0430.2851.0000.1800.0310.0460.1190.0890.1190.0740.1980.0340.0000.0420.0080.0120.0200.0170.0180.0350.1610.0800.1770.1490.1800.0700.0530.0680.0940.0320.0400.0320.0140.6450.5520.6830.4190.4620.4890.1810.4110.6160.3810.2840.2440.1610.1140.1770.0280.0970.0950.0480.0080.0000.0300.0170.0160.0120.0220.0350.0260.0370.0340.0320.0180.0540.0630.0000.0090.0370.0090.0420.0000.1030.0240.0480.0600.0740.1190.0180.0170.0380.0280.0140.0360.0270.0640.0710.0460.0290.0180.0060.0120.0090.0120.0250.0060.0710.0400.0871.0000.0120.0030.0080.0110.0160.0900.0550.007
cat1040.0000.3860.1170.3520.2730.1380.5020.3130.2210.3780.4070.4760.4840.3620.0560.0250.0420.0600.0290.0240.0400.0450.0130.0250.0510.0280.0360.0720.0320.0220.0310.0320.0170.0150.0130.0030.0000.0000.0370.0240.0190.0130.0320.0170.0090.0090.0220.1360.0130.0020.0680.0190.0620.0100.0120.0180.0200.1680.0160.0330.0150.0110.0120.0770.0100.0320.0240.0170.0370.0180.0540.0590.0220.0030.0000.0090.0130.0000.0000.0000.0190.0270.0150.0000.0080.0190.4380.0520.0110.0050.1040.0210.0000.0050.0260.0390.0220.0210.0220.0310.0050.3820.0250.3030.0000.0130.0350.0350.0100.1830.2480.3990.5740.3900.2030.0330.0210.0090.0121.0000.1880.4010.2900.2950.0090.0150.237
cat1050.0030.1630.0760.2020.1050.0850.2130.2340.0990.1680.2210.3480.3510.2220.0260.1850.0340.0340.0180.0040.0140.0180.0180.0290.0310.0160.0210.0310.0170.0090.0000.0220.0060.0070.0220.0000.0000.0000.0180.0160.0120.0130.0150.0140.0160.0190.0080.0340.0060.0000.0110.0140.0250.0140.0100.0240.0260.0350.0310.0260.0290.0000.0000.0150.0100.0210.0180.0000.0160.0150.0120.0100.0210.0000.0010.0000.0090.0000.0000.0150.0030.0310.0190.0000.0060.0000.1440.0480.0140.0100.0690.0140.0000.0000.0180.0200.0220.0190.0270.0130.0000.2630.0120.0790.0060.0100.0170.0130.0130.1290.1480.1640.2710.1650.2000.0180.0080.0090.0030.1881.0000.2360.2660.2590.0020.0060.355
cat1060.0000.3740.0800.1760.1810.1400.5440.4050.1780.3740.4350.4110.4180.3320.0330.0560.0510.0600.0340.0200.0250.0380.0130.0180.0540.0260.0360.0710.0340.0130.0110.0380.0150.0130.0060.0000.0000.0000.0350.0210.0190.0120.0220.0170.0100.0100.0170.0860.0000.0060.0480.0210.0490.0050.0170.0160.0140.1040.0180.0290.0120.0000.0090.0480.0030.0240.0210.0120.0270.0170.0380.0380.0160.0110.0050.0110.0060.0000.0000.0160.0100.0180.0220.0030.0010.0070.2980.0630.0140.0040.0980.0130.0000.0060.0270.0390.0230.0260.0270.0250.0040.2610.0270.1830.0060.0130.0320.0310.0110.1030.1760.2140.6820.2460.1690.0320.0160.0050.0080.4010.2361.0000.4110.3030.0030.0100.318
cat1070.0000.3670.1170.1630.1660.1290.3250.2600.1950.3650.3530.2130.2100.3870.0380.0570.0320.0460.0310.0220.0280.0540.0170.0280.0400.0280.0400.0540.0330.0190.0110.0350.0130.0060.0220.0000.0000.0000.0210.0120.0160.0160.0210.0130.0100.0140.0250.1010.0080.0000.0420.0110.0360.0200.0270.0200.0180.1180.0210.0350.0200.0100.0080.0570.0190.0420.0520.0180.0500.0120.0370.0380.0240.0130.0070.0060.0090.0000.0000.0170.0130.0260.0360.0000.0000.0120.1350.0220.0210.0160.0960.0240.0090.0100.0170.0260.0170.0140.0170.0250.0050.5420.0210.2450.0090.0110.0180.0150.0090.1510.4210.0880.5760.5580.2840.0220.0140.0100.0110.2900.2660.4111.0000.4280.0020.0140.544
cat1080.0030.4030.0610.2270.2370.1590.4420.4330.2200.4800.4510.2500.2530.5860.0610.0350.0360.0680.0330.0340.0490.0660.0160.0450.0590.0370.0470.0820.0410.0190.0370.0370.0140.0130.0350.0090.0000.0090.0380.0300.0250.0560.0370.0240.0200.0200.0300.2230.0120.0060.0920.0280.0700.0200.0230.0330.0330.2660.0220.0670.0260.0070.0140.1030.0170.0460.0570.0280.0680.0220.0770.0870.0190.0030.0110.0120.0090.0000.0030.0080.0150.0500.0200.0000.0050.0310.1830.0610.0210.0120.0780.0230.0070.0090.0290.0420.0330.0190.0310.0330.0070.8800.0260.5350.0070.0130.0340.0330.0110.2090.7140.1360.5470.9940.6350.0400.0250.0160.0160.2950.2590.3030.4281.0000.0120.0240.483
cat1110.1580.0070.0060.0080.0080.0040.0110.0090.0000.0110.0070.0100.0090.0120.0110.0220.0670.0770.0351.0000.2830.1490.0390.0470.0790.0620.2050.0640.1030.0220.0000.0400.0290.0090.0270.0190.0210.0390.6610.4960.6870.5870.5420.4750.3830.4070.4500.1740.2880.2670.1550.1500.1850.1750.0860.0710.0830.0410.0700.1740.0560.0360.0310.0170.1100.1450.0210.0970.0820.0360.0090.0070.0310.0220.0200.0230.0360.0530.0380.0500.0350.0330.0340.0680.0790.0000.0130.0890.0320.0210.0000.1010.0210.0440.0700.1010.1310.0200.0160.0430.0280.0180.0450.0090.1160.0750.0320.0260.0080.0000.0080.0030.0050.0170.0070.0680.1550.1270.0900.0090.0020.0030.0020.0121.0000.1610.000
cat1140.0000.0140.0340.0180.0150.0110.0220.0190.0160.0170.0200.0190.0190.0250.0200.0230.0990.4380.0600.1340.1691.0000.0510.0660.4100.2280.1930.2310.1880.0450.0000.1050.0370.0280.1250.0130.0230.0250.2370.0990.1560.0610.1710.1080.0380.0330.0760.0380.0290.0140.0000.2420.1240.1600.0730.1130.1060.0390.0170.0960.0270.0320.0140.0090.6100.9270.3220.6080.7210.4880.2270.2010.0570.0210.0460.0100.0240.0240.0290.0240.0380.0660.0260.0140.0200.0300.0180.0840.0450.0220.0150.0990.0210.0250.0870.1160.0840.0470.0430.0260.0280.0410.0920.0210.0510.0550.0610.0370.0410.0230.0250.0130.0140.0370.0180.1460.1060.0730.0550.0150.0060.0100.0140.0240.1611.0000.016
cat1150.0020.2730.1050.2190.1820.1470.3590.2620.2270.2990.3510.2370.2340.5120.0410.1610.0310.0510.0170.0170.0240.0590.0100.0350.0410.0290.0450.0690.0380.0170.0140.0210.0130.0140.0130.0000.0000.0000.0240.0200.0210.0300.0150.0150.0130.0150.0280.1020.0140.0080.0460.0120.0370.0200.0230.0240.0250.1210.0190.0420.0180.0000.0070.0520.0210.0360.0530.0270.0590.0200.0400.0400.0170.0030.0000.0080.0090.0000.0000.0140.0140.0390.0360.0000.0000.0110.1270.0240.0200.0120.1130.0270.0070.0100.0210.0310.0190.0100.0170.0270.0060.6790.0210.2530.0040.0000.0200.0190.0040.1880.4940.1740.3600.5450.3750.0230.0150.0120.0070.2370.3550.3180.5440.4830.0000.0161.000

Missing values

2023-09-24T21:59:44.843828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-24T21:59:47.692979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idcat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cat19cat20cat21cat22cat23cat24cat25cat26cat27cat28cat29cat30cat31cat32cat33cat34cat35cat36cat37cat38cat39cat40cat41cat42cat43cat44cat45cat46cat47cat48cat49cat50cat51cat52cat53cat54cat55cat56cat57cat58cat59cat60cat61cat62cat63cat64cat65cat66cat67cat68cat69cat70cat71cat72cat73cat74cat75cat76cat77cat78cat79cat80cat81cat82cat83cat84cat85cat86cat87cat88cat89cat90cat91cat92cat93cat94cat95cat96cat97cat98cat99cat100cat101cat102cat103cat104cat105cat106cat107cat108cat109cat110cat111cat112cat113cat114cat115cat116cont1cont2cont3cont4cont5cont6cont7cont8cont9cont10cont11cont12cont13cont14lossUnnamed: 132
01ABABAAAABABAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABADBBDDBDCBDBAAAAADBCEACTBGAAIEGJGBUBCCASSAOLB0.7263000.2459210.1875830.7896390.3100610.7183670.3350600.302600.671350.835100.5697450.5946460.8224930.7148432213.18NaN
12ABAAAAAABBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADBBDDABCBDBAAAAADDCEEDTLFAAEEIKKBICQAAVBMAODP0.3305140.7370680.5926810.6141340.8858340.4389170.4365850.600870.351270.439190.3383120.3663070.6114310.3044961283.60NaN
25ABAABAAABBBBBAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADBBBDBDCBBBAAAAADDCEEADLOABEFHFAABDKACAFAIGK0.2618410.3583190.4841960.2369240.3970690.2896480.3155450.273200.260760.324460.3813980.3734240.1957090.7744253005.09NaN
310BBABAAAABAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAADBBDDDBCBDBAAAAADDCEEDTIDAAEEIKKBICSCNAEAODJ0.3215940.5557820.5279910.3738160.4222680.4409450.3911280.317960.321280.444670.3279150.3215700.6050770.602642939.85NaN
411ABABAAAABBABAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBDBDBBCBBCAAABHDBDEEAPFJAADEKGBHCCYBMAKCK0.2732040.1599900.5279910.4732020.7042680.1781930.2474080.245640.220890.212300.2046870.2022130.2460110.4326062763.85NaN
513ABAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBDBDBBCBBBAAAAADDDECAPJDAAEEHFBBICSAASAEAKDJ0.5466700.6817610.6342240.3738160.3026780.3644640.4011620.268470.462260.505560.3667880.3592490.3452470.7267925142.87NaN
614AAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADBBDDBDCBBBAAAAADDDECAPJAACEEHFBBIDKAJAFAKDJ0.4714470.7370680.6136600.1891370.2953970.3815150.3637680.245640.404550.472250.3348280.3522510.3422390.3829311132.22NaN
720ABABAAAABAAAAAAAAAAAAABAAAABAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADBDBDABCBDCDAAAACBCEACTHCAAKFFIGBIEBGAHYAPLO0.8265910.4887890.2635700.6237700.4737670.8670210.5833890.902670.848470.802180.6440130.7857060.8597640.2424163585.75NaN
823ABBBBAAABBBBBAABAAAAAABAAAAAAAAAAAABABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBDBBBBCBDDAABAADCCECDTCQACHFGMKBIBCCKAXAQIE0.3305140.5557820.4406420.4732020.2811430.6285340.3840990.612290.382490.511110.6823150.6690330.7564540.36119110280.20NaN
924ABAABBAABAAAAAAAAAAAAAAAAAAAAAAAAAABABBBABAAAAAAABABAAAAAAAAAAAAAAAAAAAAAAAADBBBBBBCBDCDAABHDBCEACTFDAFKHGJGBUDWAUSJOLY0.7263000.3583190.3568190.8028920.3100610.7133430.4692230.302600.671350.835100.8630520.8793470.8224930.2945236184.59NaN
idcat1cat2cat3cat4cat5cat6cat7cat8cat9cat10cat11cat12cat13cat14cat15cat16cat17cat18cat19cat20cat21cat22cat23cat24cat25cat26cat27cat28cat29cat30cat31cat32cat33cat34cat35cat36cat37cat38cat39cat40cat41cat42cat43cat44cat45cat46cat47cat48cat49cat50cat51cat52cat53cat54cat55cat56cat57cat58cat59cat60cat61cat62cat63cat64cat65cat66cat67cat68cat69cat70cat71cat72cat73cat74cat75cat76cat77cat78cat79cat80cat81cat82cat83cat84cat85cat86cat87cat88cat89cat90cat91cat92cat93cat94cat95cat96cat97cat98cat99cat100cat101cat102cat103cat104cat105cat106cat107cat108cat109cat110cat111cat112cat113cat114cat115cat116cont1cont2cont3cont4cont5cont6cont7cont8cont9cont10cont11cont12cont13cont14lossUnnamed: 132
194582587606AAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADBBDBBBABBBAAAGHDBDEEAPFAACDEKHBABEGAAIBMAMCK0.3254010.5557820.5497700.4732020.7587110.2011250.2593950.245640.308590.219830.2072380.2046870.3574000.3482172161.12\n
194583587607ABABBBAABAAAAAAAAAAAAABAAAAAAAAAAAABAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAABAAAADBDBDBBCBBBAAACADCCEEAKKDABEEHEAKAICJAWEIDJ0.3066030.2991020.3568190.3273540.3970690.2695200.3389630.339060.280660.305290.2454100.2617990.1814330.3985714080.42\n
194584587611ABAABAAABAABAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAADBBDDBBCBBBAAAAADBDEEAPGFABCEKHBKEBAJAEALCI0.2896180.3583190.5061050.3548930.3026780.1862540.3172740.277970.321280.243550.1804560.1786980.3043500.3816604659.57\n
194585587612AAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABBABABAAAAAAAAAAAAAAAAAAAAAAAAAAAAABADBBBDBDCBBBAAABHDDDEAAPFAAEEEFFBABEBAJMAJGK0.5851150.4887890.5497700.6044080.2811430.5027050.4738970.435180.662010.582570.4150290.4060900.3543440.377315994.85\n
194586587619AAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAADBBDDBBCBDBAAAAADDCEEDTKAAAEEINKBIEGAASAEEQDJ0.2286060.7370680.5279910.3738160.4222680.4450080.3779300.366360.290950.444670.3279150.3215700.7310590.721499804.28\n
194587587620ABAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABBAAADBCBDBBCBBBAAAAADDDEEAPIDAADEJGBBIEGAGBMALCK0.3474030.7857840.6136600.4732020.9395560.2424370.2899490.245640.308590.329350.2230380.2200030.3332920.2082161198.62\n
194588587624AAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAADBBDDBACBBBAAAAADDDECAPKAAAEEHFBBIBTAAVAUEJDF0.5076610.5557820.5497700.8028920.7042680.3342700.3820000.634750.404550.477790.3076280.3019210.3186460.3058721108.34\n
194589587630ABAAAAABBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAADBDBCBACBBBAAABHDDDECAPFCCAEFHFBBIDMAWAFAKDJ0.4844690.7857840.7923780.1891370.4824360.3458830.3705340.245640.458080.477790.4456140.4433740.3392440.5038885762.64\n
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